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backend/README.md

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+# Voicebox Backend
+
+FastAPI server powering voice cloning, speech generation, and audio processing. Runs locally as a Tauri sidecar or standalone via `python -m backend.main`.
+
+## Running
+
+```bash
+# Via justfile (recommended)
+just dev:server
+
+# Standalone
+python -m backend.main --host 127.0.0.1 --port 17493
+
+# With custom data directory
+python -m backend.main --data-dir /path/to/data
+```
+
+The server auto-initializes the SQLite database on first startup. Models are downloaded from HuggingFace on first use.
+
+## Architecture
+
+```
+backend/
+  app.py                  # FastAPI app factory, CORS, lifecycle events
+  main.py                 # Entry point (imports app, runs uvicorn)
+  config.py               # Data directory paths and configuration
+  models.py               # Pydantic request/response schemas
+  server.py               # Tauri sidecar launcher, parent-pid watchdog
+
+  routes/                 # Thin HTTP handlers — validation, delegation, response formatting
+  services/               # Business logic, CRUD, orchestration
+  backends/               # TTS/STT engine implementations (MLX, PyTorch, etc.)
+  database/               # ORM models, session management, migrations, seed data
+  utils/                  # Shared utilities (audio, effects, caching, progress tracking)
+```
+
+### Request flow
+
+```
+HTTP request
+  -> routes/        (validate input, parse params)
+  -> services/      (business logic, database queries, orchestration)
+  -> backends/      (TTS/STT inference)
+  -> utils/         (audio processing, effects, caching)
+```
+
+Route handlers are intentionally thin. They validate input, delegate to a service function, and format the response. All business logic lives in `services/`.
+
+### Key modules
+
+**services/generation.py** -- Single `run_generation()` function that handles all three generation modes (generate, retry, regenerate). Manages model loading, voice prompt creation, chunked inference, normalization, effects, and version persistence.
+
+**services/task_queue.py** -- Serial generation queue. Ensures only one GPU inference runs at a time. Background tasks are tracked to prevent garbage collection.
+
+**backends/__init__.py** -- Protocol definitions (`TTSBackend`, `STTBackend`), model config registry, and factory functions. Adding a new engine means implementing the protocol and registering a config entry.
+
+**backends/base.py** -- Shared utilities used across all engine implementations: HuggingFace cache checks, device detection, voice prompt combination, progress tracking.
+
+**database/** -- SQLAlchemy ORM models with a re-exporting `__init__.py` for backward compatibility. Migrations run automatically on startup.
+
+### Backend selection
+
+The server detects the best inference backend at startup:
+
+| Platform | Backend | Acceleration |
+|----------|---------|-------------|
+| macOS (Apple Silicon) | MLX | Metal / Neural Engine |
+| Windows / Linux (NVIDIA) | PyTorch | CUDA |
+| Linux (AMD) | PyTorch | ROCm |
+| Intel Arc | PyTorch | IPEX / XPU |
+| Windows (any GPU) | PyTorch | DirectML |
+| Any | PyTorch | CPU fallback |
+
+Detection is handled by `utils/platform_detect.py`. Both backends implement the same `TTSBackend` protocol, so the API layer is engine-agnostic.
+
+## API
+
+90 endpoints organized by domain. Full interactive documentation available at `http://localhost:17493/docs` when the server is running.
+
+| Domain | Prefix | Description |
+|--------|--------|-------------|
+| Health | `/`, `/health` | Server status, GPU info, filesystem checks |
+| Profiles | `/profiles` | Voice profile CRUD, samples, avatars, import/export |
+| Channels | `/channels` | Audio channel management and voice assignment |
+| Generation | `/generate` | TTS generation, retry, regenerate, status SSE |
+| History | `/history` | Generation history, search, favorites, export |
+| Transcription | `/transcribe` | Whisper-based audio-to-text |
+| Stories | `/stories` | Multi-track timeline editor, audio export |
+| Effects | `/effects` | Effect presets, preview, version management |
+| Audio | `/audio`, `/samples` | Audio file serving |
+| Models | `/models` | Load, unload, download, migrate, status |
+| Tasks | `/tasks`, `/cache` | Active task tracking, cache management |
+| CUDA | `/backend/cuda-*` | CUDA binary download and management |
+
+### Quick examples
+
+```bash
+# Generate speech
+curl -X POST http://localhost:17493/generate \
+  -H "Content-Type: application/json" \
+  -d '{"text": "Hello world", "profile_id": "...", "language": "en"}'
+
+# List profiles
+curl http://localhost:17493/profiles
+
+# Stream generation status (SSE)
+curl http://localhost:17493/generate/{id}/status
+```
+
+## Data directory
+
+```
+{data_dir}/
+  voicebox.db             # SQLite database
+  profiles/{id}/          # Voice samples per profile
+  generations/            # Generated audio files
+  cache/                  # Voice prompt cache (memory + disk)
+  backends/               # Downloaded CUDA binary (if applicable)
+```
+
+Default location is the OS-specific app data directory. Override with `--data-dir` or the `VOICEBOX_DATA_DIR` environment variable.
+
+## Code quality
+
+Linting and formatting are enforced by [ruff](https://docs.astral.sh/ruff/), configured in `pyproject.toml`. See `STYLE_GUIDE.md` for conventions.
+
+```bash
+just check-python       # lint + format check
+just fix-python         # auto-fix lint issues + reformat
+just test               # run pytest
+```
+
+## Dependencies
+
+Runtime dependencies are in `requirements.txt`. macOS-only MLX dependencies are in `requirements-mlx.txt`. Dev tools (ruff, pytest) are installed automatically by `just setup-python`.

backend/STYLE_GUIDE.md

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+# Python Style Guide
+
+Target: **Python 3.12+** | Formatter/Linter: **Ruff** | Config: `backend/pyproject.toml`
+
+This guide codifies the conventions used across the backend, and prescribes the target style for code written during the refactor (Phases 3-6). Existing code should be migrated incrementally -- don't reformat entire files in unrelated PRs.
+
+---
+
+## Formatting
+
+Enforced by `ruff format` (Black-compatible).
+
+- **Line length**: 120 characters.
+- **Indent**: 4 spaces. No tabs.
+- **Trailing commas**: Required on multi-line function signatures, arguments, collections.
+- **Quotes**: Double quotes (`"`) for strings. Single quotes are acceptable in f-string expressions and dict keys inside f-strings where avoiding escapes improves readability.
+
+Run: `ruff format backend/`
+
+---
+
+## Imports
+
+Enforced by ruff's `isort` rules (rule set `I`).
+
+**Grouping** -- three blocks separated by a blank line:
+
+```python
+import asyncio                          # 1. stdlib
+from pathlib import Path
+
+import numpy as np                      # 2. third-party
+from fastapi import APIRouter, HTTPException
+from sqlalchemy.orm import Session
+
+from backend.config import get_data_dir  # 3. local (absolute)
+from .database import get_db            #    or relative
+```
+
+**Rules:**
+- Within the `backend` package, use **relative imports** for sibling/child modules: `from .database import get_db`, `from ..utils.audio import load_audio`.
+- Absolute imports are fine for top-level references from entry points (`main.py`, `server.py`).
+- Never use wildcard imports (`from module import *`).
+- One import per line for `from X import Y` when there are 4+ names; below that, comma-separated is fine.
+- **Lazy imports** are acceptable for heavy dependencies (torch, transformers, mlx) inside functions to reduce startup time. Add a comment: `# lazy: heavy import`.
+
+---
+
+## Type Annotations
+
+Python 3.12 means we use **built-in generics and union syntax natively**. No `from __future__ import annotations`, no `typing.List`/`typing.Dict`.
+
+```python
+# Yes
+def process(items: list[str], config: dict[str, int] | None = None) -> tuple[int, str]: ...
+
+# No
+from typing import List, Dict, Optional, Tuple
+def process(items: List[str], config: Optional[Dict[str, int]] = None) -> Tuple[int, str]: ...
+```
+
+**What to annotate:**
+- All public function signatures (parameters + return type).
+- Private functions: parameters at minimum; return type encouraged.
+- Module-level variables: only when the type isn't obvious from the assignment.
+- Route handlers: parameters are annotated via FastAPI's dependency injection. Add explicit `-> SomeResponse` return types when the route doesn't use `response_model`.
+
+**Imports from `typing` that are still needed** (no built-in equivalent):
+`Literal`, `TypeAlias`, `Protocol`, `runtime_checkable`, `Callable`, `Any`, `ClassVar`, `TypeVar`, `overload`, `TYPE_CHECKING`.
+
+Use `collections.abc` for abstract types: `Sequence`, `Mapping`, `Iterable`, `Iterator`, `Generator`.
+
+---
+
+## Naming
+
+| Thing | Convention | Example |
+|-------|-----------|---------|
+| Module | `snake_case` | `task_queue.py` |
+| Class | `PascalCase` | `ProgressManager` |
+| Function / method | `snake_case` | `create_profile` |
+| Variable | `snake_case` | `sample_rate` |
+| Constant | `UPPER_SNAKE_CASE` | `DEFAULT_SAMPLE_RATE` |
+| Private | `_leading_underscore` | `_generation_queue` |
+| Type alias | `PascalCase` | `EffectChain = list[dict[str, Any]]` |
+
+**Specific conventions:**
+- Database ORM models imported with `DB` prefix alias: `from .database import VoiceProfile as DBVoiceProfile`.
+- Pydantic models use descriptive suffixes: `VoiceProfileCreate`, `VoiceProfileResponse`, `GenerationRequest`.
+- Backend classes use engine-name prefix: `MLXTTSBackend`, `PyTorchSTTBackend`.
+
+---
+
+## Docstrings
+
+**Google style**. Required on all public functions, classes, and modules.
+
+```python
+def combine_voice_prompts(
+    profile_dir: Path,
+    *,
+    target_sr: int = 24000,
+) -> tuple[np.ndarray, int]:
+    """Load and concatenate all voice prompt files for a profile.
+
+    Reads .wav/.mp3/.flac files from the profile directory, resamples to
+    the target sample rate, normalizes, and concatenates into a single array.
+
+    Args:
+        profile_dir: Path to the voice profile directory containing audio files.
+        target_sr: Target sample rate for the output. Defaults to 24000.
+
+    Returns:
+        Tuple of (concatenated audio array, sample rate).
+
+    Raises:
+        FileNotFoundError: If profile_dir does not exist.
+        ValueError: If no valid audio files are found.
+    """
+```
+
+**Short form** is fine for simple functions:
+
+```python
+def get_db_path() -> Path:
+    """Get the path to the SQLite database file."""
+```
+
+**When to skip**: Private helpers under ~5 lines where the name and signature make intent obvious.
+
+**Module docstrings**: A single sentence at the top of every file describing its purpose.
+
+```python
+"""Voice profile CRUD operations."""
+```
+
+---
+
+## Comments
+
+Comments explain **why**, not **what**. If the code needs a comment to explain what it does, the code should be rewritten to be clearer. The exceptions are non-obvious performance choices, external constraints, and concurrency/race-condition reasoning -- those always deserve a comment.
+
+### No section dividers
+
+Do not use ASCII dividers to create visual sections in files:
+
+```python
+# No -- any of these:
+# ============================================
+# GENERATION ENDPOINTS
+# ============================================
+
+# ---------------------------------------------------------------------------
+# Device detection
+# ---------------------------------------------------------------------------
+
+# --- Load model --------------------------------------------------
+```
+
+If a file needs section dividers to be navigable, the file is too long. Split it into modules. Within a function, if you need labeled sections to follow the logic, extract those sections into named functions.
+
+### Inline comments
+
+Inline comments (end-of-line) are fine when they add information the code can't express:
+
+```python
+# Yes -- explains a non-obvious constraint or gives context:
+audio, sr = load_audio(path, sr=24000)  # Qwen expects 24kHz mono
+_generation_queue: asyncio.Queue = None  # type: ignore  # initialized at startup
+"tauri://localhost",         # Tauri webview (macOS)
+
+# No -- restates the code:
+# Check if profile name already exists
+existing = db.query(DBVoiceProfile).filter_by(name=data.name).first()
+
+# Delete from database
+db.delete(sample)
+
+# Update fields
+profile.name = data.name
+```
+
+Delete comments that narrate what the next line of code obviously does. If the function name, variable name, or method call already communicates intent, the comment is noise.
+
+### Block comments
+
+Use block comments for **why** explanations -- constraints, workarounds, non-obvious decisions:
+
+```python
+# PyInstaller + multiprocessing: child processes re-execute the frozen binary
+# with internal arguments. freeze_support() handles this and exits early.
+multiprocessing.freeze_support()
+
+# Mark any stale "generating" records as failed -- these are leftovers
+# from a previous process that was killed mid-generation.
+db.query(Generation).filter_by(status="generating").update({"status": "failed"})
+```
+
+Keep block comments tight. Two to three lines is normal. If you need a paragraph, it probably belongs in the docstring or a design doc.
+
+### Linter/type-checker suppression
+
+Always add a reason after `noqa` and `type: ignore`:
+
+```python
+import intel_extension_for_pytorch  # noqa: F401 -- side-effect import enables XPU
+_queue: asyncio.Queue = None  # type: ignore[assignment]  # initialized at startup
+```
+
+Bare `# noqa` or `# type: ignore` with no explanation are not allowed.
+
+### TODO / FIXME
+
+Use sparingly. Every `TODO` must include a brief description of what needs doing. Don't use them as a substitute for tracking work properly:
+
+```python
+# TODO: replace with async SQLAlchemy once CRUD modules are migrated (Phase 5)
+result = await asyncio.to_thread(profiles.get_profile, profile_id, db)
+```
+
+Never commit `HACK`, `XXX`, or `FIXME` -- fix the problem or file an issue.
+
+### Commented-out code
+
+Delete it. That's what git is for. If you need to document that something was intentionally removed, a short tombstone comment is acceptable:
+
+```python
+# Removed config.json-only check -- too lenient, doesn't confirm weights exist.
+```
+
+---
+
+## Error Handling
+
+The refactor is standardizing on a **two-layer pattern**:
+
+### 1. Domain layer -- raise plain exceptions
+
+CRUD modules and services raise `ValueError`, `FileNotFoundError`, or (post-refactor) custom exceptions defined in `backend/errors.py`:
+
+```python
+# backend/errors.py  (to be created in Phase 4)
+class NotFoundError(Exception):
+    """Raised when a requested resource does not exist."""
+
+class ConflictError(Exception):
+    """Raised on uniqueness constraint violations."""
+```
+
+```python
+# In a service or CRUD module:
+raise NotFoundError(f"Profile {profile_id} not found")
+```
+
+### 2. Route layer -- translate to HTTPException
+
+Route handlers catch domain exceptions and convert:
+
+```python
+@router.post("/profiles")
+async def create_profile(data: VoiceProfileCreate, db: Session = Depends(get_db)):
+    try:
+        return await profiles.create_profile(data, db)
+    except ConflictError as e:
+        raise HTTPException(status_code=409, detail=str(e))
+```
+
+**Background tasks** catch `Exception` broadly, log with `logger.exception()`, and update the task status to `"failed"`.
+
+**Never**: silently swallow exceptions, use bare `except:`, or catch `BaseException`.
+
+---
+
+## Async
+
+### Rules for the refactor
+
+1. **Don't declare `async def` unless the function awaits something.** Several service modules still declare `async def` without awaiting -- these should be migrated to sync functions with `asyncio.to_thread()` at the route layer, or to real async SQLAlchemy.
+2. **CPU-bound work** (audio processing, numpy operations) goes through `asyncio.to_thread()`:
+   ```python
+   audio, sr = await asyncio.to_thread(load_audio, source_path)
+   ```
+3. **GPU-bound TTS inference** is serialized through the generation queue (`services/task_queue.py`). Never call a backend's `generate()` directly from a route handler.
+4. **Fire-and-forget tasks**: use `asyncio.create_task()` and track the task reference to prevent garbage collection:
+   ```python
+   task = asyncio.create_task(some_coro())
+   _background_tasks.add(task)
+   task.add_done_callback(_background_tasks.discard)
+   ```
+
+---
+
+## Logging
+
+Use the `logging` module. Not `print()`.
+
+```python
+import logging
+
+logger = logging.getLogger(__name__)
+
+logger.info("Loading model %s on %s", model_name, device)
+logger.warning("Cache miss for %s, downloading", repo_id)
+logger.exception("Generation %s failed")  # logs traceback automatically
+```
+
+**Rules:**
+- Use `%s`-style placeholders in log calls (not f-strings). This avoids formatting the string if the log level is filtered out.
+- Use `logger.exception()` inside `except` blocks -- it captures the traceback.
+- Logger name should be `__name__` (yields `backend.utils.audio`, etc.).
+- Existing `print()` calls should be migrated to logging as files are touched during the refactor.
+
+---
+
+## Constants
+
+- Define at **module level** in the file where they're primarily used.
+- Use `UPPER_SNAKE_CASE`.
+- Shared/cross-cutting constants (sample rates, file size limits, CORS origins) go in `backend/config.py` after Phase 6 consolidation.
+- Magic numbers in function bodies should be extracted to named constants:
+  ```python
+  # No
+  if len(audio) > 24000 * 60 * 10:
+
+  # Yes
+  MAX_AUDIO_DURATION_SAMPLES = SAMPLE_RATE * 60 * 10
+  if len(audio) > MAX_AUDIO_DURATION_SAMPLES:
+  ```
+
+---
+
+## Function Signatures
+
+- **Keyword-only arguments** (after `*`) for functions with 3+ parameters, especially when several share the same type:
+  ```python
+  def is_model_cached(
+      hf_repo: str,
+      *,
+      weight_extensions: tuple[str, ...] = (".safetensors", ".bin"),
+      required_files: list[str] | None = None,
+  ) -> bool:
+  ```
+- Parameters on **separate lines** when the signature exceeds ~100 characters or has 3+ params.
+- **Trailing comma** after the last parameter in multi-line signatures.
+- Default values inline with the parameter.
+
+---
+
+## String Formatting
+
+- **f-strings** for runtime string construction.
+- **`%s`-style** for `logging` calls (lazy evaluation).
+- **`.format()`**: avoid; f-strings are preferred.
+
+---
+
+## Testing
+
+Framework: **pytest** with `pytest-asyncio`.
+
+- Test files: `test_<module>.py` in `backend/tests/`.
+- Use `conftest.py` for shared fixtures (db sessions, test client, mock backends).
+- Group related tests in classes: `class TestProfileCRUD:`.
+- Use `@pytest.mark.asyncio` for async tests.
+- Use `@pytest.mark.parametrize` to reduce repetition.
+- Manual integration scripts stay in `tests/` but are clearly marked (filename prefix `manual_` or documented in `tests/README.md`).
+
+---
+
+## Project Layout
+
+```
+backend/
+  app.py              # FastAPI app factory, CORS, lifecycle events
+  main.py             # Entry point (imports app, runs uvicorn)
+  config.py           # Data directory paths
+  models.py           # Pydantic request/response schemas
+  server.py           # Tauri sidecar launcher, parent-pid watchdog
+  routes/             # Thin HTTP handlers (validation, delegation, response formatting)
+  services/           # Business logic, CRUD, orchestration
+  backends/           # TTS/STT engine implementations
+  database/           # ORM models, session management, migrations, seeds
+  utils/              # Shared utilities (audio, effects, caching, progress)
+  tests/              # pytest suite
+```
+
+---
+
+## Ruff Adoption
+
+`pyproject.toml` configures ruff for linting and formatting. Run:
+
+```bash
+# Lint (check)
+ruff check backend/
+
+# Lint (auto-fix)
+ruff check backend/ --fix
+
+# Format
+ruff format backend/
+```
+
+Introduce ruff fixes file-by-file as you touch them. Don't run `--fix` across the entire codebase in one shot -- that creates unreviewable diffs.

backend/__init__.py

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+# Backend package
+
+__version__ = "0.4.5"

backend/app.py

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+"""FastAPI application factory, middleware, and lifecycle events."""
+
+import asyncio
+import logging
+import os
+import sys
+from pathlib import Path
+
+
+class ColoredFormatter(logging.Formatter):
+    """Custom formatter to add colors matching uvicorn's style."""
+
+    COLORS = {
+        "DEBUG": "\033[36m",  # Cyan
+        "INFO": "\033[32m",  # Green
+        "WARNING": "\033[33m",  # Yellow
+        "ERROR": "\033[31m",  # Red
+        "CRITICAL": "\033[35m",  # Magenta
+    }
+    RESET = "\033[0m"
+
+    def format(self, record):
+        log_color = self.COLORS.get(record.levelname, self.RESET)
+        record.levelname = f"{log_color}{record.levelname}{self.RESET}"
+        return super().format(record)
+
+
+# Configure logging to match uvicorn's format with colors
+handler = logging.StreamHandler(sys.stderr)
+handler.setFormatter(ColoredFormatter("%(levelname)s:     %(message)s"))
+logging.basicConfig(
+    level=logging.INFO,
+    handlers=[handler],
+)
+
+logger = logging.getLogger(__name__)
+
+# AMD GPU environment variables must be set before torch import
+if not os.environ.get("HSA_OVERRIDE_GFX_VERSION"):
+    os.environ["HSA_OVERRIDE_GFX_VERSION"] = "10.3.0"
+if not os.environ.get("MIOPEN_LOG_LEVEL"):
+    os.environ["MIOPEN_LOG_LEVEL"] = "4"
+
+import torch
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from urllib.parse import quote
+
+from . import __version__, config, database
+from .services import tts, transcribe
+from .database import get_db
+from .utils.platform_detect import get_backend_type
+from .utils.progress import get_progress_manager
+from .services.task_queue import create_background_task, init_queue
+from .routes import register_routers
+
+
+def safe_content_disposition(disposition_type: str, filename: str) -> str:
+    """Build a Content-Disposition header safe for non-ASCII filenames.
+
+    Uses RFC 5987 ``filename*`` parameter so browsers can decode UTF-8
+    filenames while the ``filename`` fallback stays ASCII-only.
+    """
+    ascii_name = "".join(c for c in filename if c.isascii() and (c.isalnum() or c in " -_.")).strip() or "download"
+    utf8_name = quote(filename, safe="")
+    return f"{disposition_type}; filename=\"{ascii_name}\"; filename*=UTF-8''{utf8_name}"
+
+
+def create_app() -> FastAPI:
+    """Create and configure the FastAPI application."""
+    application = FastAPI(
+        title="voicebox API",
+        description="Production-quality Qwen3-TTS voice cloning API",
+        version=__version__,
+    )
+
+    _configure_cors(application)
+    register_routers(application)
+    _register_lifecycle(application)
+    _mount_frontend(application)
+
+    return application
+
+
+def _configure_cors(application: FastAPI) -> None:
+    """Set up CORS middleware with local-first defaults."""
+    default_origins = [
+        "http://localhost:5173",  # Vite dev server
+        "http://127.0.0.1:5173",
+        "http://localhost:17493",
+        "http://127.0.0.1:17493",
+        "tauri://localhost",  # Tauri webview (macOS)
+        "https://tauri.localhost",  # Tauri webview (Windows/Linux)
+        "http://tauri.localhost",  # Tauri webview (Windows, some builds)
+    ]
+    env_origins = os.environ.get("VOICEBOX_CORS_ORIGINS", "")
+    all_origins = default_origins + [o.strip() for o in env_origins.split(",") if o.strip()]
+
+    application.add_middleware(
+        CORSMiddleware,
+        allow_origins=all_origins,
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+
+
+def _mount_frontend(application: FastAPI) -> None:
+    """Serve the built web frontend when present (Docker / web deployment).
+
+    The Dockerfile copies the Vite build output to ``/app/frontend/``.  When
+    that directory exists we mount static assets and add a catch-all route so
+    the React SPA handles client-side routing.  In dev or API-only mode the
+    directory is absent and this function is a no-op.
+    """
+    frontend_dir = Path(__file__).resolve().parent.parent / "frontend"
+    if not frontend_dir.is_dir():
+        return
+
+    from fastapi.staticfiles import StaticFiles
+    from fastapi.responses import FileResponse
+
+    # Mount hashed assets (JS, CSS, images) that Vite places under /assets
+    assets_dir = frontend_dir / "assets"
+    if assets_dir.is_dir():
+        application.mount(
+            "/assets",
+            StaticFiles(directory=str(assets_dir)),
+            name="frontend-assets",
+        )
+
+    # SPA catch-all: serve files if they exist, otherwise index.html for
+    # client-side routes like /voices, /stories, /models, etc.
+    @application.get("/{full_path:path}")
+    async def serve_spa(full_path: str):
+        file_path = (frontend_dir / full_path).resolve()
+        # Guard against path traversal — only serve files inside frontend_dir
+        if full_path and file_path.is_file() and file_path.is_relative_to(frontend_dir):
+            return FileResponse(file_path)
+        return FileResponse(frontend_dir / "index.html", media_type="text/html")
+
+    logger.info("Frontend: serving SPA from %s", frontend_dir)
+
+
+def _get_gpu_status() -> str:
+    """Return a human-readable string describing GPU availability."""
+    backend_type = get_backend_type()
+    if torch.cuda.is_available():
+        from .backends.base import check_cuda_compatibility
+
+        device_name = torch.cuda.get_device_name(0)
+        compatible, _warning = check_cuda_compatibility()
+        is_rocm = hasattr(torch.version, "hip") and torch.version.hip is not None
+        if is_rocm:
+            label = f"ROCm ({device_name})"
+        else:
+            label = f"CUDA ({device_name})"
+        if not compatible:
+            label += " [UNSUPPORTED - see logs]"
+        return label
+    elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+        return "MPS (Apple Silicon)"
+    elif backend_type == "mlx":
+        return "Metal (Apple Silicon via MLX)"
+
+    # Intel XPU (Arc / Data Center) via IPEX
+    try:
+        import intel_extension_for_pytorch  # noqa: F401
+
+        if hasattr(torch, "xpu") and torch.xpu.is_available():
+            try:
+                xpu_name = torch.xpu.get_device_name(0)
+            except Exception:
+                xpu_name = "Intel GPU"
+            return f"XPU ({xpu_name})"
+    except ImportError:
+        pass
+
+    return "None (CPU only)"
+
+
+def _register_lifecycle(application: FastAPI) -> None:
+    """Attach startup and shutdown event handlers."""
+
+    @application.on_event("startup")
+    async def startup_event():
+        import platform
+        import sys
+
+        logger.info("Voicebox v%s starting up", __version__)
+        logger.info(
+            "Python %s on %s %s (%s)",
+            sys.version.split()[0],
+            platform.system(),
+            platform.release(),
+            platform.machine(),
+        )
+
+        database.init_db()
+
+        from .database.session import _db_path
+
+        logger.info("Database: %s", _db_path)
+        logger.info("Data directory: %s", config.get_data_dir())
+
+        init_queue()
+
+        # Mark stale "generating" records as failed -- leftovers from a killed process
+        from sqlalchemy import text as sa_text
+
+        db = next(get_db())
+        try:
+            result = db.execute(
+                sa_text(
+                    "UPDATE generations SET status = 'failed', "
+                    "error = 'Server was shut down during generation' "
+                    "WHERE status IN ('generating', 'loading_model')"
+                )
+            )
+            if result.rowcount > 0:
+                logger.info("Marked %d stale generation(s) as failed", result.rowcount)
+
+            from .database import VoiceProfile as DBVoiceProfile, Generation as DBGeneration
+
+            profile_count = db.query(DBVoiceProfile).count()
+            generation_count = db.query(DBGeneration).count()
+            logger.info("Profiles: %d, Generations: %d", profile_count, generation_count)
+
+            db.commit()
+        except Exception as e:
+            db.rollback()
+            logger.warning("Could not clean up stale generations: %s", e)
+        finally:
+            db.close()
+
+        backend_type = get_backend_type()
+        logger.info("Backend: %s", backend_type.upper())
+        logger.info("GPU: %s", _get_gpu_status())
+
+        # Warn if GPU architecture is not supported by this PyTorch build
+        from .backends.base import check_cuda_compatibility
+
+        _compatible, _cuda_warning = check_cuda_compatibility()
+        if not _compatible:
+            logger.warning("GPU COMPATIBILITY: %s", _cuda_warning)
+
+        from .services.cuda import check_and_update_cuda_binary
+
+        create_background_task(check_and_update_cuda_binary())
+
+        try:
+            progress_manager = get_progress_manager()
+            progress_manager._set_main_loop(asyncio.get_running_loop())
+        except Exception as e:
+            logger.warning("Could not initialize progress manager event loop: %s", e)
+
+        try:
+            from huggingface_hub import constants as hf_constants
+
+            cache_dir = Path(hf_constants.HF_HUB_CACHE)
+            cache_dir.mkdir(parents=True, exist_ok=True)
+            logger.info("Model cache: %s", cache_dir)
+        except Exception as e:
+            logger.warning("Could not create HuggingFace cache directory: %s", e)
+
+        logger.info("Ready")
+
+    @application.on_event("shutdown")
+    async def shutdown_event():
+        logger.info("Voicebox server shutting down...")
+        try:
+            tts.unload_tts_model()
+        except Exception:
+            logger.exception("Failed to unload TTS model")
+        try:
+            transcribe.unload_whisper_model()
+        except Exception:
+            logger.exception("Failed to unload Whisper model")
+
+
+app = create_app()

backend/backends/__init__.py

@@ -0,0 +1,621 @@
+"""
+Backend abstraction layer for TTS and STT.
+
+Provides a unified interface for MLX and PyTorch backends,
+and a model config registry that eliminates per-engine dispatch maps.
+"""
+
+# Install HF compatibility patches before any backend imports transformers /
+# huggingface_hub. The module runs ``patch_transformers_mistral_regex`` at
+# import time, which wraps transformers' tokenizer load against the
+# unconditional HuggingFace metadata call that otherwise raises on
+# HF_HUB_OFFLINE=1 and on network failures.
+from ..utils import hf_offline_patch  # noqa: F401
+
+import threading
+from dataclasses import dataclass, field
+from typing import Protocol, Optional, Tuple, List
+from typing_extensions import runtime_checkable
+import numpy as np
+
+from ..utils.platform_detect import get_backend_type
+
+LANGUAGE_CODE_TO_NAME = {
+    "zh": "chinese",
+    "en": "english",
+    "ja": "japanese",
+    "ko": "korean",
+    "de": "german",
+    "fr": "french",
+    "ru": "russian",
+    "pt": "portuguese",
+    "es": "spanish",
+    "it": "italian",
+}
+
+WHISPER_HF_REPOS = {
+    "base": "openai/whisper-base",
+    "small": "openai/whisper-small",
+    "medium": "openai/whisper-medium",
+    "large": "openai/whisper-large-v3",
+    "turbo": "openai/whisper-large-v3-turbo",
+}
+
+
+@dataclass
+class ModelConfig:
+    """Declarative config for a downloadable model variant."""
+
+    model_name: str  # e.g. "luxtts", "chatterbox-tts"
+    display_name: str  # e.g. "LuxTTS (Fast, CPU-friendly)"
+    engine: str  # e.g. "luxtts", "chatterbox"
+    hf_repo_id: str  # e.g. "YatharthS/LuxTTS"
+    model_size: str = "default"
+    size_mb: int = 0
+    needs_trim: bool = False
+    supports_instruct: bool = False
+    languages: list[str] = field(default_factory=lambda: ["en"])
+
+
+@runtime_checkable
+class TTSBackend(Protocol):
+    """Protocol for TTS backend implementations."""
+
+    # Each backend class should define MODEL_CONFIGS as a class variable:
+    # MODEL_CONFIGS: list[ModelConfig]
+
+    async def load_model(self, model_size: str) -> None:
+        """Load TTS model."""
+        ...
+
+    async def create_voice_prompt(
+        self,
+        audio_path: str,
+        reference_text: str,
+        use_cache: bool = True,
+    ) -> Tuple[dict, bool]:
+        """
+        Create voice prompt from reference audio.
+
+        Returns:
+            Tuple of (voice_prompt_dict, was_cached)
+        """
+        ...
+
+    async def combine_voice_prompts(
+        self,
+        audio_paths: List[str],
+        reference_texts: List[str],
+    ) -> Tuple[np.ndarray, str]:
+        """
+        Combine multiple voice prompts.
+
+        Returns:
+            Tuple of (combined_audio_array, combined_text)
+        """
+        ...
+
+    async def generate(
+        self,
+        text: str,
+        voice_prompt: dict,
+        language: str = "en",
+        seed: Optional[int] = None,
+        instruct: Optional[str] = None,
+    ) -> Tuple[np.ndarray, int]:
+        """
+        Generate audio from text.
+
+        Returns:
+            Tuple of (audio_array, sample_rate)
+        """
+        ...
+
+    def unload_model(self) -> None:
+        """Unload model to free memory."""
+        ...
+
+    def is_loaded(self) -> bool:
+        """Check if model is loaded."""
+        ...
+
+    def _get_model_path(self, model_size: str) -> str:
+        """
+        Get model path for a given size.
+
+        Returns:
+            Model path or HuggingFace Hub ID
+        """
+        ...
+
+
+@runtime_checkable
+class STTBackend(Protocol):
+    """Protocol for STT (Speech-to-Text) backend implementations."""
+
+    async def load_model(self, model_size: str) -> None:
+        """Load STT model."""
+        ...
+
+    async def transcribe(
+        self,
+        audio_path: str,
+        language: Optional[str] = None,
+        model_size: Optional[str] = None,
+    ) -> str:
+        """
+        Transcribe audio to text.
+
+        Returns:
+            Transcribed text
+        """
+        ...
+
+    def unload_model(self) -> None:
+        """Unload model to free memory."""
+        ...
+
+    def is_loaded(self) -> bool:
+        """Check if model is loaded."""
+        ...
+
+
+# Global backend instances
+_tts_backend: Optional[TTSBackend] = None
+_tts_backends: dict[str, TTSBackend] = {}
+_tts_backends_lock = threading.Lock()
+_stt_backend: Optional[STTBackend] = None
+
+# Supported TTS engines — keyed by engine name, value is the backend class import path.
+# The factory function uses this for the if/elif chain; the model configs live on the backend classes.
+TTS_ENGINES = {
+    "qwen": "Qwen TTS",
+    "qwen_custom_voice": "Qwen CustomVoice",
+    "luxtts": "LuxTTS",
+    "chatterbox": "Chatterbox TTS",
+    "chatterbox_turbo": "Chatterbox Turbo",
+    "tada": "TADA",
+    "kokoro": "Kokoro",
+}
+
+
+def _get_qwen_model_configs() -> list[ModelConfig]:
+    """Return Qwen model configs with backend-aware HF repo IDs."""
+    backend_type = get_backend_type()
+    if backend_type == "mlx":
+        repo_1_7b = "mlx-community/Qwen3-TTS-12Hz-1.7B-Base-bf16"
+        repo_0_6b = "mlx-community/Qwen3-TTS-12Hz-0.6B-Base-bf16"
+    else:
+        repo_1_7b = "Qwen/Qwen3-TTS-12Hz-1.7B-Base"
+        repo_0_6b = "Qwen/Qwen3-TTS-12Hz-0.6B-Base"
+
+    return [
+        ModelConfig(
+            model_name="qwen-tts-1.7B",
+            display_name="Qwen TTS 1.7B",
+            engine="qwen",
+            hf_repo_id=repo_1_7b,
+            model_size="1.7B",
+            size_mb=3500,
+            supports_instruct=False,  # Base model drops instruct silently
+            languages=["zh", "en", "ja", "ko", "de", "fr", "ru", "pt", "es", "it"],
+        ),
+        ModelConfig(
+            model_name="qwen-tts-0.6B",
+            display_name="Qwen TTS 0.6B",
+            engine="qwen",
+            hf_repo_id=repo_0_6b,
+            model_size="0.6B",
+            size_mb=1200,
+            supports_instruct=False,
+            languages=["zh", "en", "ja", "ko", "de", "fr", "ru", "pt", "es", "it"],
+        ),
+    ]
+
+
+def _get_qwen_custom_voice_configs() -> list[ModelConfig]:
+    """Return Qwen CustomVoice model configs."""
+    return [
+        ModelConfig(
+            model_name="qwen-custom-voice-1.7B",
+            display_name="Qwen CustomVoice 1.7B",
+            engine="qwen_custom_voice",
+            hf_repo_id="Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice",
+            model_size="1.7B",
+            size_mb=3500,
+            supports_instruct=True,
+            languages=["zh", "en", "ja", "ko", "de", "fr", "ru", "pt", "es", "it"],
+        ),
+        ModelConfig(
+            model_name="qwen-custom-voice-0.6B",
+            display_name="Qwen CustomVoice 0.6B",
+            engine="qwen_custom_voice",
+            hf_repo_id="Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice",
+            model_size="0.6B",
+            size_mb=1200,
+            supports_instruct=True,
+            languages=["zh", "en", "ja", "ko", "de", "fr", "ru", "pt", "es", "it"],
+        ),
+    ]
+
+
+def _get_non_qwen_tts_configs() -> list[ModelConfig]:
+    """Return model configs for non-Qwen TTS engines.
+
+    These are static — no backend-type branching needed.
+    """
+    return [
+        ModelConfig(
+            model_name="luxtts",
+            display_name="LuxTTS (Fast, CPU-friendly)",
+            engine="luxtts",
+            hf_repo_id="YatharthS/LuxTTS",
+            size_mb=300,
+            languages=["en"],
+        ),
+        ModelConfig(
+            model_name="chatterbox-tts",
+            display_name="Chatterbox TTS (Multilingual)",
+            engine="chatterbox",
+            hf_repo_id="ResembleAI/chatterbox",
+            size_mb=3200,
+            needs_trim=True,
+            languages=[
+                "zh",
+                "en",
+                "ja",
+                "ko",
+                "de",
+                "fr",
+                "ru",
+                "pt",
+                "es",
+                "it",
+                "he",
+                "ar",
+                "da",
+                "el",
+                "fi",
+                "hi",
+                "ms",
+                "nl",
+                "no",
+                "pl",
+                "sv",
+                "sw",
+                "tr",
+            ],
+        ),
+        ModelConfig(
+            model_name="chatterbox-turbo",
+            display_name="Chatterbox Turbo (English, Tags)",
+            engine="chatterbox_turbo",
+            hf_repo_id="ResembleAI/chatterbox-turbo",
+            size_mb=1500,
+            needs_trim=True,
+            languages=["en"],
+        ),
+        ModelConfig(
+            model_name="tada-1b",
+            display_name="TADA 1B (English)",
+            engine="tada",
+            hf_repo_id="HumeAI/tada-1b",
+            model_size="1B",
+            size_mb=4000,
+            languages=["en"],
+        ),
+        ModelConfig(
+            model_name="tada-3b-ml",
+            display_name="TADA 3B Multilingual",
+            engine="tada",
+            hf_repo_id="HumeAI/tada-3b-ml",
+            model_size="3B",
+            size_mb=8000,
+            languages=["en", "ar", "zh", "de", "es", "fr", "it", "ja", "pl", "pt"],
+        ),
+        ModelConfig(
+            model_name="kokoro",
+            display_name="Kokoro 82M",
+            engine="kokoro",
+            hf_repo_id="hexgrad/Kokoro-82M",
+            size_mb=350,
+            languages=["en", "es", "fr", "hi", "it", "pt", "ja", "zh"],
+        ),
+    ]
+
+
+def _get_whisper_configs() -> list[ModelConfig]:
+    """Return Whisper STT model configs."""
+    return [
+        ModelConfig(
+            model_name="whisper-base",
+            display_name="Whisper Base",
+            engine="whisper",
+            hf_repo_id="openai/whisper-base",
+            model_size="base",
+        ),
+        ModelConfig(
+            model_name="whisper-small",
+            display_name="Whisper Small",
+            engine="whisper",
+            hf_repo_id="openai/whisper-small",
+            model_size="small",
+        ),
+        ModelConfig(
+            model_name="whisper-medium",
+            display_name="Whisper Medium",
+            engine="whisper",
+            hf_repo_id="openai/whisper-medium",
+            model_size="medium",
+        ),
+        ModelConfig(
+            model_name="whisper-large",
+            display_name="Whisper Large",
+            engine="whisper",
+            hf_repo_id="openai/whisper-large-v3",
+            model_size="large",
+        ),
+        ModelConfig(
+            model_name="whisper-turbo",
+            display_name="Whisper Turbo",
+            engine="whisper",
+            hf_repo_id="openai/whisper-large-v3-turbo",
+            model_size="turbo",
+        ),
+    ]
+
+
+def get_all_model_configs() -> list[ModelConfig]:
+    """Return the full list of model configs (TTS + STT)."""
+    return _get_qwen_model_configs() + _get_qwen_custom_voice_configs() + _get_non_qwen_tts_configs() + _get_whisper_configs()
+
+
+def get_tts_model_configs() -> list[ModelConfig]:
+    """Return only TTS model configs."""
+    return _get_qwen_model_configs() + _get_qwen_custom_voice_configs() + _get_non_qwen_tts_configs()
+
+
+# Lookup helpers — these replace the if/elif chains in main.py
+
+
+def get_model_config(model_name: str) -> Optional[ModelConfig]:
+    """Look up a model config by model_name."""
+    for cfg in get_all_model_configs():
+        if cfg.model_name == model_name:
+            return cfg
+    return None
+
+
+def engine_needs_trim(engine: str) -> bool:
+    """Whether this engine's output should be run through trim_tts_output."""
+    for cfg in get_tts_model_configs():
+        if cfg.engine == engine:
+            return cfg.needs_trim
+    return False
+
+
+def engine_has_model_sizes(engine: str) -> bool:
+    """Whether this engine supports multiple model sizes (only Qwen currently)."""
+    configs = [c for c in get_tts_model_configs() if c.engine == engine]
+    return len(configs) > 1
+
+
+async def load_engine_model(engine: str, model_size: str = "default") -> None:
+    """Load a model for the given engine, handling engines with multiple model sizes."""
+    backend = get_tts_backend_for_engine(engine)
+    if engine in ("qwen", "qwen_custom_voice"):
+        await backend.load_model_async(model_size)
+    elif engine == "tada":
+        await backend.load_model(model_size)
+    else:
+        await backend.load_model()
+
+
+async def ensure_model_cached_or_raise(engine: str, model_size: str = "default") -> None:
+    """Check if a model is cached, raise HTTPException if not. Used by streaming endpoint."""
+    from fastapi import HTTPException
+
+    backend = get_tts_backend_for_engine(engine)
+    cfg = None
+    for c in get_tts_model_configs():
+        if c.engine == engine and c.model_size == model_size:
+            cfg = c
+            break
+
+    if engine in ("qwen", "qwen_custom_voice", "tada"):
+        if not backend._is_model_cached(model_size):
+            raise HTTPException(
+                status_code=400,
+                detail=f"Model {model_size} is not downloaded yet. Use /generate to trigger a download.",
+            )
+    else:
+        if not backend._is_model_cached():
+            display = cfg.display_name if cfg else engine
+            raise HTTPException(
+                status_code=400,
+                detail=f"{display} model is not downloaded yet. Use /generate to trigger a download.",
+            )
+
+
+def unload_model_by_config(config: ModelConfig) -> bool:
+    """Unload a model given its config. Returns True if it was loaded, False otherwise."""
+    from . import get_tts_backend_for_engine
+    from ..services import tts, transcribe
+
+    if config.engine == "whisper":
+        whisper_model = transcribe.get_whisper_model()
+        if whisper_model.is_loaded() and whisper_model.model_size == config.model_size:
+            transcribe.unload_whisper_model()
+            return True
+        return False
+
+    if config.engine == "qwen":
+        tts_model = tts.get_tts_model()
+        loaded_size = getattr(tts_model, "_current_model_size", None) or getattr(tts_model, "model_size", None)
+        if tts_model.is_loaded() and loaded_size == config.model_size:
+            tts.unload_tts_model()
+            return True
+        return False
+
+    if config.engine == "qwen_custom_voice":
+        backend = get_tts_backend_for_engine(config.engine)
+        loaded_size = getattr(backend, "_current_model_size", None) or getattr(backend, "model_size", None)
+        if backend.is_loaded() and loaded_size == config.model_size:
+            backend.unload_model()
+            return True
+        return False
+
+    # All other TTS engines
+    backend = get_tts_backend_for_engine(config.engine)
+    if backend.is_loaded():
+        backend.unload_model()
+        return True
+    return False
+
+
+def check_model_loaded(config: ModelConfig) -> bool:
+    """Check if a model is currently loaded."""
+    from . import get_tts_backend_for_engine
+    from ..services import tts, transcribe
+
+    try:
+        if config.engine == "whisper":
+            whisper_model = transcribe.get_whisper_model()
+            return whisper_model.is_loaded() and getattr(whisper_model, "model_size", None) == config.model_size
+
+        if config.engine == "qwen":
+            tts_model = tts.get_tts_model()
+            loaded_size = getattr(tts_model, "_current_model_size", None) or getattr(tts_model, "model_size", None)
+            return tts_model.is_loaded() and loaded_size == config.model_size
+
+        if config.engine == "qwen_custom_voice":
+            backend = get_tts_backend_for_engine(config.engine)
+            loaded_size = getattr(backend, "_current_model_size", None) or getattr(backend, "model_size", None)
+            return backend.is_loaded() and loaded_size == config.model_size
+
+        backend = get_tts_backend_for_engine(config.engine)
+        return backend.is_loaded()
+    except Exception:
+        return False
+
+
+def get_model_load_func(config: ModelConfig):
+    """Return a callable that loads/downloads the model."""
+    from . import get_tts_backend_for_engine
+    from ..services import tts, transcribe
+
+    if config.engine == "whisper":
+        return lambda: transcribe.get_whisper_model().load_model(config.model_size)
+
+    if config.engine == "qwen":
+        return lambda: tts.get_tts_model().load_model(config.model_size)
+
+    if config.engine == "qwen_custom_voice":
+        return lambda: get_tts_backend_for_engine(config.engine).load_model(config.model_size)
+
+    return lambda: get_tts_backend_for_engine(config.engine).load_model()
+
+
+def get_tts_backend() -> TTSBackend:
+    """
+    Get or create the default (Qwen) TTS backend instance based on platform.
+
+    Returns:
+        TTS backend instance (MLX or PyTorch)
+    """
+    return get_tts_backend_for_engine("qwen")
+
+
+def get_tts_backend_for_engine(engine: str) -> TTSBackend:
+    """
+    Get or create a TTS backend for the given engine.
+
+    Args:
+        engine: Engine name (e.g. "qwen", "luxtts", "chatterbox", "chatterbox_turbo")
+
+    Returns:
+        TTS backend instance
+    """
+    global _tts_backends
+
+    # Fast path: check without lock
+    if engine in _tts_backends:
+        return _tts_backends[engine]
+
+    # Slow path: create with lock to avoid duplicate instantiation
+    with _tts_backends_lock:
+        # Double-check after acquiring lock
+        if engine in _tts_backends:
+            return _tts_backends[engine]
+
+        if engine == "qwen":
+            backend_type = get_backend_type()
+            if backend_type == "mlx":
+                from .mlx_backend import MLXTTSBackend
+
+                backend = MLXTTSBackend()
+            else:
+                from .pytorch_backend import PyTorchTTSBackend
+
+                backend = PyTorchTTSBackend()
+        elif engine == "luxtts":
+            from .luxtts_backend import LuxTTSBackend
+
+            backend = LuxTTSBackend()
+        elif engine == "chatterbox":
+            from .chatterbox_backend import ChatterboxTTSBackend
+
+            backend = ChatterboxTTSBackend()
+        elif engine == "chatterbox_turbo":
+            from .chatterbox_turbo_backend import ChatterboxTurboTTSBackend
+
+            backend = ChatterboxTurboTTSBackend()
+        elif engine == "tada":
+            from .hume_backend import HumeTadaBackend
+
+            backend = HumeTadaBackend()
+        elif engine == "kokoro":
+            from .kokoro_backend import KokoroTTSBackend
+
+            backend = KokoroTTSBackend()
+        elif engine == "qwen_custom_voice":
+            from .qwen_custom_voice_backend import QwenCustomVoiceBackend
+
+            backend = QwenCustomVoiceBackend()
+        else:
+            raise ValueError(f"Unknown TTS engine: {engine}. Supported: {list(TTS_ENGINES.keys())}")
+
+        _tts_backends[engine] = backend
+        return backend
+
+
+def get_stt_backend() -> STTBackend:
+    """
+    Get or create STT backend instance based on platform.
+
+    Returns:
+        STT backend instance (MLX or PyTorch)
+    """
+    global _stt_backend
+
+    if _stt_backend is None:
+        backend_type = get_backend_type()
+
+        if backend_type == "mlx":
+            from .mlx_backend import MLXSTTBackend
+
+            _stt_backend = MLXSTTBackend()
+        else:
+            from .pytorch_backend import PyTorchSTTBackend
+
+            _stt_backend = PyTorchSTTBackend()
+
+    return _stt_backend
+
+
+def reset_backends():
+    """Reset backend instances (useful for testing)."""
+    global _tts_backend, _tts_backends, _stt_backend
+    _tts_backend = None
+    _tts_backends.clear()
+    _stt_backend = None

backend/backends/base.py

@@ -0,0 +1,327 @@
+"""
+Shared utilities for TTS/STT backend implementations.
+
+Eliminates duplication of cache checking, device detection,
+voice prompt combination, and model loading progress tracking.
+"""
+
+import logging
+import platform
+from contextlib import contextmanager
+from pathlib import Path
+from typing import Callable, List, Optional, Tuple
+
+import numpy as np
+
+from ..utils.audio import normalize_audio, load_audio
+from ..utils.progress import get_progress_manager
+from ..utils.hf_progress import HFProgressTracker, create_hf_progress_callback
+from ..utils.tasks import get_task_manager
+
+logger = logging.getLogger(__name__)
+
+
+def is_model_cached(
+    hf_repo: str,
+    *,
+    weight_extensions: tuple[str, ...] = (".safetensors", ".bin"),
+    required_files: Optional[list[str]] = None,
+) -> bool:
+    """
+    Check if a HuggingFace model is fully cached locally.
+
+    Args:
+        hf_repo: HuggingFace repo ID (e.g. "Qwen/Qwen3-TTS-12Hz-1.7B-Base")
+        weight_extensions: File extensions that count as model weights.
+        required_files: If set, check that these specific filenames exist
+                        in snapshots instead of checking by extension.
+
+    Returns:
+        True if model is fully cached, False if missing or incomplete.
+    """
+    try:
+        from huggingface_hub import constants as hf_constants
+
+        repo_cache = Path(hf_constants.HF_HUB_CACHE) / ("models--" + hf_repo.replace("/", "--"))
+
+        if not repo_cache.exists():
+            return False
+
+        # Incomplete blobs mean a download is still in progress
+        blobs_dir = repo_cache / "blobs"
+        if blobs_dir.exists() and any(blobs_dir.glob("*.incomplete")):
+            logger.debug(f"Found .incomplete files for {hf_repo}")
+            return False
+
+        snapshots_dir = repo_cache / "snapshots"
+        if not snapshots_dir.exists():
+            return False
+
+        if required_files:
+            # Check that every required filename exists somewhere in snapshots
+            for fname in required_files:
+                if not any(snapshots_dir.rglob(fname)):
+                    return False
+            return True
+
+        # Check that at least one weight file exists
+        for ext in weight_extensions:
+            if any(snapshots_dir.rglob(f"*{ext}")):
+                return True
+
+        logger.debug(f"No model weights found for {hf_repo}")
+        return False
+
+    except Exception as e:
+        logger.warning(f"Error checking cache for {hf_repo}: {e}")
+        return False
+
+
+def get_torch_device(
+    *,
+    allow_xpu: bool = False,
+    allow_directml: bool = False,
+    allow_mps: bool = False,
+    force_cpu_on_mac: bool = False,
+) -> str:
+    """
+    Detect the best available torch device.
+
+    Args:
+        allow_xpu: Check for Intel XPU (IPEX) support.
+        allow_directml: Check for DirectML (Windows) support.
+        allow_mps: Allow MPS (Apple Silicon). If False, MPS falls back to CPU.
+        force_cpu_on_mac: Force CPU on macOS regardless of GPU availability.
+    """
+    if force_cpu_on_mac and platform.system() == "Darwin":
+        return "cpu"
+
+    import torch
+
+    if torch.cuda.is_available():
+        return "cuda"
+
+    if allow_xpu:
+        try:
+            import intel_extension_for_pytorch  # noqa: F401
+
+            if hasattr(torch, "xpu") and torch.xpu.is_available():
+                return "xpu"
+        except ImportError:
+            pass
+
+    if allow_directml:
+        try:
+            import torch_directml
+
+            if torch_directml.device_count() > 0:
+                return torch_directml.device(0)
+        except ImportError:
+            pass
+
+    if allow_mps:
+        if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+            return "mps"
+
+    return "cpu"
+
+
+def check_cuda_compatibility() -> tuple[bool, str | None]:
+    """Check if the installed PyTorch supports the current GPU's compute capability.
+
+    Returns:
+        (compatible, warning_message) — compatible is True if OK or no CUDA GPU,
+        warning_message is a human-readable string if there's a problem.
+    """
+    import torch
+
+    if not torch.cuda.is_available():
+        return True, None
+
+    major, minor = torch.cuda.get_device_capability(0)
+    capability = f"{major}.{minor}"
+    device_name = torch.cuda.get_device_name(0)
+    sm_tag = f"sm_{major}{minor}"
+
+    # torch.cuda._get_arch_list() returns the SM architectures this build
+    # was compiled for (e.g. ["sm_50", "sm_60", ..., "sm_90"]).
+    try:
+        arch_list = torch.cuda._get_arch_list()
+        if arch_list:
+            # Check for both sm_XX and compute_XX (JIT-compiled) entries
+            compute_tag = f"compute_{major}{minor}"
+            if sm_tag not in arch_list and compute_tag not in arch_list:
+                return False, (
+                    f"{device_name} (compute capability {capability} / {sm_tag}) "
+                    f"is not supported by this PyTorch build. "
+                    f"Supported architectures: {', '.join(arch_list)}. "
+                    f"Install PyTorch nightly (cu128) for newer GPU support: "
+                    f"pip install torch --index-url https://download.pytorch.org/whl/nightly/cu128"
+                )
+    except AttributeError:
+        pass
+
+    return True, None
+
+
+def empty_device_cache(device: str) -> None:
+    """
+    Free cached memory on the given device (CUDA or XPU).
+
+    Backends should call this after unloading models so VRAM is returned
+    to the OS.
+    """
+    import torch
+
+    if device == "cuda" and torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    elif device == "xpu" and hasattr(torch, "xpu"):
+        torch.xpu.empty_cache()
+
+
+def manual_seed(seed: int, device: str) -> None:
+    """
+    Set the random seed on both CPU and the active accelerator.
+
+    Covers CUDA and Intel XPU so that generation is reproducible
+    regardless of which GPU backend is in use.
+    """
+    import torch
+
+    torch.manual_seed(seed)
+    if device == "cuda" and torch.cuda.is_available():
+        torch.cuda.manual_seed(seed)
+    elif device == "xpu" and hasattr(torch, "xpu"):
+        torch.xpu.manual_seed(seed)
+
+
+async def combine_voice_prompts(
+    audio_paths: List[str],
+    reference_texts: List[str],
+    *,
+    sample_rate: Optional[int] = None,
+) -> Tuple[np.ndarray, str]:
+    """
+    Combine multiple reference audio samples into one.
+
+    Loads each audio file, normalizes, concatenates, and joins texts.
+
+    Args:
+        audio_paths: Paths to reference audio files.
+        reference_texts: Corresponding transcripts.
+        sample_rate: If set, resample audio to this rate during loading.
+    """
+    combined_audio = []
+
+    for path in audio_paths:
+        kwargs = {"sample_rate": sample_rate} if sample_rate else {}
+        audio, _sr = load_audio(path, **kwargs)
+        audio = normalize_audio(audio)
+        combined_audio.append(audio)
+
+    mixed = np.concatenate(combined_audio)
+    mixed = normalize_audio(mixed)
+    combined_text = " ".join(reference_texts)
+
+    return mixed, combined_text
+
+
+@contextmanager
+def model_load_progress(
+    model_name: str,
+    is_cached: bool,
+    filter_non_downloads: Optional[bool] = None,
+):
+    """
+    Context manager for model loading with HF download progress tracking.
+
+    Handles the tqdm patching, progress_manager/task_manager lifecycle,
+    and error reporting that every backend duplicates.
+
+    Args:
+        model_name: Progress tracking key (e.g. "qwen-tts-1.7B", "whisper-base").
+        is_cached: Whether the model is already downloaded.
+        filter_non_downloads: Whether to filter non-download tqdm bars.
+                              Defaults to `is_cached`.
+
+    Yields:
+        The tracker context (already entered). The caller loads the model
+        inside the `with` block. The tqdm patch is torn down on exit.
+
+    Usage:
+        with model_load_progress("qwen-tts-1.7B", is_cached) as ctx:
+            self.model = SomeModel.from_pretrained(...)
+    """
+    if filter_non_downloads is None:
+        filter_non_downloads = is_cached
+
+    progress_manager = get_progress_manager()
+    task_manager = get_task_manager()
+
+    progress_callback = create_hf_progress_callback(model_name, progress_manager)
+    tracker = HFProgressTracker(progress_callback, filter_non_downloads=filter_non_downloads)
+
+    tracker_context = tracker.patch_download()
+    tracker_context.__enter__()
+
+    if not is_cached:
+        task_manager.start_download(model_name)
+        progress_manager.update_progress(
+            model_name=model_name,
+            current=0,
+            total=0,
+            filename="Connecting to HuggingFace...",
+            status="downloading",
+        )
+
+    try:
+        yield tracker_context
+    except Exception as e:
+        # Report error to both managers
+        progress_manager.mark_error(model_name, str(e))
+        task_manager.error_download(model_name, str(e))
+        raise
+    else:
+        # Only mark complete if we were tracking a download
+        if not is_cached:
+            progress_manager.mark_complete(model_name)
+            task_manager.complete_download(model_name)
+    finally:
+        tracker_context.__exit__(None, None, None)
+
+
+def patch_chatterbox_f32(model) -> None:
+    """
+    Patch float64 -> float32 dtype mismatches in upstream chatterbox.
+
+    librosa.load returns float64 numpy arrays. Multiple upstream code paths
+    convert these to torch tensors via torch.from_numpy() without casting,
+    then matmul against float32 model weights. This patches the two known
+    entry points:
+
+    1. S3Tokenizer.log_mel_spectrogram — audio tensor hits _mel_filters (f32)
+    2. VoiceEncoder.forward — float64 mel spectrograms hit LSTM weights (f32)
+    """
+    import types
+
+    # Patch S3Tokenizer
+    _tokzr = model.s3gen.tokenizer
+    _orig_log_mel = _tokzr.log_mel_spectrogram.__func__
+
+    def _f32_log_mel(self_tokzr, audio, padding=0):
+        import torch as _torch
+
+        if _torch.is_tensor(audio):
+            audio = audio.float()
+        return _orig_log_mel(self_tokzr, audio, padding)
+
+    _tokzr.log_mel_spectrogram = types.MethodType(_f32_log_mel, _tokzr)
+
+    # Patch VoiceEncoder
+    _ve = model.ve
+    _orig_ve_forward = _ve.forward.__func__
+
+    def _f32_ve_forward(self_ve, mels):
+        return _orig_ve_forward(self_ve, mels.float())
+
+    _ve.forward = types.MethodType(_f32_ve_forward, _ve)

backend/backends/chatterbox_backend.py

@@ -0,0 +1,226 @@
+"""
+Chatterbox TTS backend implementation.
+
+Wraps ChatterboxMultilingualTTS from chatterbox-tts for zero-shot
+voice cloning. Supports 23 languages including Hebrew. Forces CPU
+on macOS due to known MPS tensor issues.
+"""
+
+import asyncio
+import logging
+import threading
+from pathlib import Path
+from typing import ClassVar, List, Optional, Tuple
+
+import numpy as np
+
+from . import TTSBackend
+from .base import (
+    is_model_cached,
+    get_torch_device,
+    empty_device_cache,
+    manual_seed,
+    combine_voice_prompts as _combine_voice_prompts,
+    model_load_progress,
+    patch_chatterbox_f32,
+)
+
+logger = logging.getLogger(__name__)
+
+CHATTERBOX_HF_REPO = "ResembleAI/chatterbox"
+
+# Files that must be present for the multilingual model
+_MTL_WEIGHT_FILES = [
+    "t3_mtl23ls_v2.safetensors",
+    "s3gen.pt",
+    "ve.pt",
+]
+
+
+class ChatterboxTTSBackend:
+    """Chatterbox Multilingual TTS backend for voice cloning."""
+
+    # Class-level lock for torch.load monkey-patching
+    _load_lock: ClassVar[threading.Lock] = threading.Lock()
+
+    def __init__(self):
+        self.model = None
+        self.model_size = "default"
+        self._device = None
+        self._model_load_lock = asyncio.Lock()
+
+    def _get_device(self) -> str:
+        return get_torch_device(force_cpu_on_mac=True, allow_xpu=True)
+
+    def is_loaded(self) -> bool:
+        return self.model is not None
+
+    def _get_model_path(self, model_size: str = "default") -> str:
+        return CHATTERBOX_HF_REPO
+
+    def _is_model_cached(self, model_size: str = "default") -> bool:
+        return is_model_cached(CHATTERBOX_HF_REPO, required_files=_MTL_WEIGHT_FILES)
+
+    async def load_model(self, model_size: str = "default") -> None:
+        """Load the Chatterbox multilingual model."""
+        if self.model is not None:
+            return
+        async with self._model_load_lock:
+            if self.model is not None:
+                return
+            await asyncio.to_thread(self._load_model_sync)
+
+    def _load_model_sync(self):
+        """Synchronous model loading."""
+        model_name = "chatterbox-tts"
+        is_cached = self._is_model_cached()
+
+        with model_load_progress(model_name, is_cached):
+            device = self._get_device()
+            self._device = device
+            logger.info(f"Loading Chatterbox Multilingual TTS on {device}...")
+
+            import torch
+            from chatterbox.mtl_tts import ChatterboxMultilingualTTS
+
+            if device == "cpu":
+                _orig_torch_load = torch.load
+
+                def _patched_load(*args, **kwargs):
+                    kwargs.setdefault("map_location", "cpu")
+                    return _orig_torch_load(*args, **kwargs)
+
+                with ChatterboxTTSBackend._load_lock:
+                    torch.load = _patched_load
+                    try:
+                        model = ChatterboxMultilingualTTS.from_pretrained(device=device)
+                    finally:
+                        torch.load = _orig_torch_load
+            else:
+                model = ChatterboxMultilingualTTS.from_pretrained(device=device)
+
+            # Fix sdpa attention for output_attentions support
+            t3_tfmr = model.t3.tfmr
+            if hasattr(t3_tfmr, "config") and hasattr(t3_tfmr.config, "_attn_implementation"):
+                t3_tfmr.config._attn_implementation = "eager"
+                for layer in getattr(t3_tfmr, "layers", []):
+                    if hasattr(layer, "self_attn"):
+                        layer.self_attn._attn_implementation = "eager"
+
+            patch_chatterbox_f32(model)
+            self.model = model
+
+        logger.info("Chatterbox Multilingual TTS loaded successfully")
+
+    def unload_model(self) -> None:
+        """Unload model to free memory."""
+        if self.model is not None:
+            device = self._device
+            del self.model
+            self.model = None
+            self._device = None
+            empty_device_cache(device)
+            logger.info("Chatterbox unloaded")
+
+    async def create_voice_prompt(
+        self,
+        audio_path: str,
+        reference_text: str,
+        use_cache: bool = True,
+    ) -> Tuple[dict, bool]:
+        """
+        Create voice prompt from reference audio.
+
+        Chatterbox processes reference audio at generation time, so the
+        prompt just stores the file path. The actual audio is loaded by
+        model.generate() via audio_prompt_path.
+        """
+        voice_prompt = {
+            "ref_audio": str(audio_path),
+            "ref_text": reference_text,
+        }
+        return voice_prompt, False
+
+    async def combine_voice_prompts(
+        self,
+        audio_paths: List[str],
+        reference_texts: List[str],
+    ) -> Tuple[np.ndarray, str]:
+        return await _combine_voice_prompts(audio_paths, reference_texts)
+
+    # Per-language generation defaults. Lower temp + higher cfg = clearer speech.
+    _LANG_DEFAULTS: ClassVar[dict] = {
+        "he": {
+            "exaggeration": 0.4,
+            "cfg_weight": 0.7,
+            "temperature": 0.65,
+            "repetition_penalty": 2.5,
+        },
+    }
+    _GLOBAL_DEFAULTS: ClassVar[dict] = {
+        "exaggeration": 0.5,
+        "cfg_weight": 0.5,
+        "temperature": 0.8,
+        "repetition_penalty": 2.0,
+    }
+
+    async def generate(
+        self,
+        text: str,
+        voice_prompt: dict,
+        language: str = "en",
+        seed: Optional[int] = None,
+        instruct: Optional[str] = None,
+    ) -> Tuple[np.ndarray, int]:
+        """
+        Generate audio using Chatterbox Multilingual TTS.
+
+        Args:
+            text: Text to synthesize
+            voice_prompt: Dict with ref_audio path
+            language: BCP-47 language code
+            seed: Random seed for reproducibility
+            instruct: Unused (protocol compatibility)
+
+        Returns:
+            Tuple of (audio_array, sample_rate)
+        """
+        await self.load_model()
+
+        ref_audio = voice_prompt.get("ref_audio")
+        if ref_audio and not Path(ref_audio).exists():
+            logger.warning(f"Reference audio not found: {ref_audio}")
+            ref_audio = None
+
+        # Merge language-specific defaults with global defaults
+        lang_defaults = self._LANG_DEFAULTS.get(language, self._GLOBAL_DEFAULTS)
+
+        def _generate_sync():
+            import torch
+
+            if seed is not None:
+                manual_seed(seed, self._device)
+
+            logger.info(f"[Chatterbox] Generating: lang={language}")
+
+            wav = self.model.generate(
+                text,
+                language_id=language,
+                audio_prompt_path=ref_audio,
+                exaggeration=lang_defaults["exaggeration"],
+                cfg_weight=lang_defaults["cfg_weight"],
+                temperature=lang_defaults["temperature"],
+                repetition_penalty=lang_defaults["repetition_penalty"],
+            )
+
+            # Convert tensor -> numpy
+            if isinstance(wav, torch.Tensor):
+                audio = wav.squeeze().cpu().numpy().astype(np.float32)
+            else:
+                audio = np.asarray(wav, dtype=np.float32)
+
+            sample_rate = getattr(self.model, "sr", None) or getattr(self.model, "sample_rate", 24000)
+
+            return audio, sample_rate
+
+        return await asyncio.to_thread(_generate_sync)

backend/backends/chatterbox_turbo_backend.py

@@ -0,0 +1,206 @@
+"""
+Chatterbox Turbo TTS backend implementation.
+
+Wraps ChatterboxTurboTTS from chatterbox-tts for fast, English-only
+voice cloning with paralinguistic tag support ([laugh], [cough], etc.).
+Forces CPU on macOS due to known MPS tensor issues.
+"""
+
+import asyncio
+import logging
+import threading
+from pathlib import Path
+from typing import ClassVar, List, Optional, Tuple
+
+import numpy as np
+
+from . import TTSBackend
+from .base import (
+    is_model_cached,
+    get_torch_device,
+    empty_device_cache,
+    manual_seed,
+    combine_voice_prompts as _combine_voice_prompts,
+    model_load_progress,
+    patch_chatterbox_f32,
+)
+
+logger = logging.getLogger(__name__)
+
+CHATTERBOX_TURBO_HF_REPO = "ResembleAI/chatterbox-turbo"
+
+# Files that must be present for the turbo model
+_TURBO_WEIGHT_FILES = [
+    "t3_turbo_v1.safetensors",
+    "s3gen_meanflow.safetensors",
+    "ve.safetensors",
+]
+
+
+class ChatterboxTurboTTSBackend:
+    """Chatterbox Turbo TTS backend — fast, English-only, with paralinguistic tags."""
+
+    # Class-level lock for torch.load monkey-patching
+    _load_lock: ClassVar[threading.Lock] = threading.Lock()
+
+    def __init__(self):
+        self.model = None
+        self.model_size = "default"
+        self._device = None
+        self._model_load_lock = asyncio.Lock()
+
+    def _get_device(self) -> str:
+        return get_torch_device(force_cpu_on_mac=True, allow_xpu=True)
+
+    def is_loaded(self) -> bool:
+        return self.model is not None
+
+    def _get_model_path(self, model_size: str = "default") -> str:
+        return CHATTERBOX_TURBO_HF_REPO
+
+    def _is_model_cached(self, model_size: str = "default") -> bool:
+        return is_model_cached(CHATTERBOX_TURBO_HF_REPO, required_files=_TURBO_WEIGHT_FILES)
+
+    async def load_model(self, model_size: str = "default") -> None:
+        """Load the Chatterbox Turbo model."""
+        if self.model is not None:
+            return
+        async with self._model_load_lock:
+            if self.model is not None:
+                return
+            await asyncio.to_thread(self._load_model_sync)
+
+    def _load_model_sync(self):
+        """Synchronous model loading."""
+        model_name = "chatterbox-turbo"
+        is_cached = self._is_model_cached()
+
+        with model_load_progress(model_name, is_cached):
+            device = self._get_device()
+            self._device = device
+            logger.info(f"Loading Chatterbox Turbo TTS on {device}...")
+
+            import torch
+            from huggingface_hub import snapshot_download
+            from chatterbox.tts_turbo import ChatterboxTurboTTS
+
+            local_path = snapshot_download(
+                repo_id=CHATTERBOX_TURBO_HF_REPO,
+                token=None,
+                allow_patterns=["*.safetensors", "*.json", "*.txt", "*.pt", "*.model"],
+            )
+
+            if device == "cpu":
+                _orig_torch_load = torch.load
+
+                def _patched_load(*args, **kwargs):
+                    kwargs.setdefault("map_location", "cpu")
+                    return _orig_torch_load(*args, **kwargs)
+
+                with ChatterboxTurboTTSBackend._load_lock:
+                    torch.load = _patched_load
+                    try:
+                        model = ChatterboxTurboTTS.from_local(local_path, device)
+                    finally:
+                        torch.load = _orig_torch_load
+            else:
+                model = ChatterboxTurboTTS.from_local(local_path, device)
+
+            patch_chatterbox_f32(model)
+            self.model = model
+
+        logger.info("Chatterbox Turbo TTS loaded successfully")
+
+    def unload_model(self) -> None:
+        """Unload model to free memory."""
+        if self.model is not None:
+            device = self._device
+            del self.model
+            self.model = None
+            self._device = None
+            empty_device_cache(device)
+            logger.info("Chatterbox Turbo unloaded")
+
+    async def create_voice_prompt(
+        self,
+        audio_path: str,
+        reference_text: str,
+        use_cache: bool = True,
+    ) -> Tuple[dict, bool]:
+        """
+        Create voice prompt from reference audio.
+
+        Chatterbox Turbo processes reference audio at generation time, so the
+        prompt just stores the file path.
+        """
+        voice_prompt = {
+            "ref_audio": str(audio_path),
+            "ref_text": reference_text,
+        }
+        return voice_prompt, False
+
+    async def combine_voice_prompts(
+        self,
+        audio_paths: List[str],
+        reference_texts: List[str],
+    ) -> Tuple[np.ndarray, str]:
+        return await _combine_voice_prompts(audio_paths, reference_texts)
+
+    async def generate(
+        self,
+        text: str,
+        voice_prompt: dict,
+        language: str = "en",
+        seed: Optional[int] = None,
+        instruct: Optional[str] = None,
+    ) -> Tuple[np.ndarray, int]:
+        """
+        Generate audio using Chatterbox Turbo TTS.
+
+        Supports paralinguistic tags in text: [laugh], [cough], [chuckle], etc.
+
+        Args:
+            text: Text to synthesize (may include paralinguistic tags)
+            voice_prompt: Dict with ref_audio path
+            language: Ignored (Turbo is English-only)
+            seed: Random seed for reproducibility
+            instruct: Unused (protocol compatibility)
+
+        Returns:
+            Tuple of (audio_array, sample_rate)
+        """
+        await self.load_model()
+
+        ref_audio = voice_prompt.get("ref_audio")
+        if ref_audio and not Path(ref_audio).exists():
+            logger.warning(f"Reference audio not found: {ref_audio}")
+            ref_audio = None
+
+        def _generate_sync():
+            import torch
+
+            if seed is not None:
+                manual_seed(seed, self._device)
+
+            logger.info("[Chatterbox Turbo] Generating (English)")
+
+            wav = self.model.generate(
+                text,
+                audio_prompt_path=ref_audio,
+                temperature=0.8,
+                top_k=1000,
+                top_p=0.95,
+                repetition_penalty=1.2,
+            )
+
+            # Convert tensor -> numpy
+            if isinstance(wav, torch.Tensor):
+                audio = wav.squeeze().cpu().numpy().astype(np.float32)
+            else:
+                audio = np.asarray(wav, dtype=np.float32)
+
+            sample_rate = getattr(self.model, "sr", None) or getattr(self.model, "sample_rate", 24000)
+
+            return audio, sample_rate
+
+        return await asyncio.to_thread(_generate_sync)

backend/backends/hume_backend.py

@@ -0,0 +1,346 @@
+"""
+HumeAI TADA TTS backend implementation.
+
+Wraps HumeAI's TADA (Text-Acoustic Dual Alignment) model for
+high-quality voice cloning. Two model variants:
+  - tada-1b: English-only, ~2B params (Llama 3.2 1B base)
+  - tada-3b-ml: Multilingual, ~4B params (Llama 3.2 3B base)
+
+Both use a shared encoder/codec (HumeAI/tada-codec). The encoder
+produces 1:1 aligned token embeddings from reference audio, and the
+causal LM generates speech via flow-matching diffusion.
+
+24kHz output, bf16 inference on CUDA, fp32 on CPU.
+"""
+
+import asyncio
+import logging
+import threading
+from typing import ClassVar, List, Optional, Tuple
+
+import numpy as np
+
+from . import TTSBackend
+from .base import (
+    is_model_cached,
+    get_torch_device,
+    empty_device_cache,
+    manual_seed,
+    combine_voice_prompts as _combine_voice_prompts,
+    model_load_progress,
+)
+from ..utils.cache import get_cache_key, get_cached_voice_prompt, cache_voice_prompt
+
+logger = logging.getLogger(__name__)
+
+# HuggingFace repos
+TADA_CODEC_REPO = "HumeAI/tada-codec"
+TADA_1B_REPO = "HumeAI/tada-1b"
+TADA_3B_ML_REPO = "HumeAI/tada-3b-ml"
+
+TADA_MODEL_REPOS = {
+    "1B": TADA_1B_REPO,
+    "3B": TADA_3B_ML_REPO,
+}
+
+# Key weight files for cache detection
+_TADA_MODEL_WEIGHT_FILES = [
+    "model.safetensors",
+]
+
+_TADA_CODEC_WEIGHT_FILES = [
+    "encoder/model.safetensors",
+]
+
+
+class HumeTadaBackend:
+    """HumeAI TADA TTS backend for high-quality voice cloning."""
+
+    _load_lock: ClassVar[threading.Lock] = threading.Lock()
+
+    def __init__(self):
+        self.model = None
+        self.encoder = None
+        self.model_size = "1B"  # default to 1B
+        self._device = None
+        self._model_load_lock = asyncio.Lock()
+
+    def _get_device(self) -> str:
+        # Force CPU on macOS — MPS has issues with flow matching
+        # and large vocab lm_head (>65536 output channels)
+        return get_torch_device(force_cpu_on_mac=True, allow_xpu=True)
+
+    def is_loaded(self) -> bool:
+        return self.model is not None
+
+    def _get_model_path(self, model_size: str = "1B") -> str:
+        return TADA_MODEL_REPOS.get(model_size, TADA_1B_REPO)
+
+    def _is_model_cached(self, model_size: str = "1B") -> bool:
+        repo = TADA_MODEL_REPOS.get(model_size, TADA_1B_REPO)
+        model_cached = is_model_cached(repo, required_files=_TADA_MODEL_WEIGHT_FILES)
+        codec_cached = is_model_cached(TADA_CODEC_REPO, required_files=_TADA_CODEC_WEIGHT_FILES)
+        return model_cached and codec_cached
+
+    async def load_model(self, model_size: str = "1B") -> None:
+        """Load the TADA model and encoder."""
+        if self.model is not None and self.model_size == model_size:
+            return
+        async with self._model_load_lock:
+            if self.model is not None and self.model_size == model_size:
+                return
+            # Unload existing model if switching sizes
+            if self.model is not None:
+                self.unload_model()
+            self.model_size = model_size
+            await asyncio.to_thread(self._load_model_sync, model_size)
+
+    def _load_model_sync(self, model_size: str = "1B"):
+        """Synchronous model loading with progress tracking."""
+        model_name = f"tada-{model_size.lower()}"
+        is_cached = self._is_model_cached(model_size)
+        repo = TADA_MODEL_REPOS.get(model_size, TADA_1B_REPO)
+
+        with model_load_progress(model_name, is_cached):
+            # Install DAC shim before importing tada — tada's encoder/decoder
+            # import dac.nn.layers.Snake1d which requires the descript-audio-codec
+            # package.  The real package pulls in onnx/tensorboard/matplotlib via
+            # descript-audiotools, so we use a lightweight shim instead.
+            from ..utils.dac_shim import install_dac_shim
+
+            install_dac_shim()
+
+            import torch
+            from huggingface_hub import snapshot_download
+
+            device = self._get_device()
+            self._device = device
+            logger.info(f"Loading HumeAI TADA {model_size} on {device}...")
+
+            # Download codec (encoder + decoder) if not cached
+            logger.info("Downloading TADA codec...")
+            snapshot_download(
+                repo_id=TADA_CODEC_REPO,
+                token=None,
+                allow_patterns=["*.safetensors", "*.json", "*.txt", "*.bin"],
+            )
+
+            # Download model weights if not cached
+            logger.info(f"Downloading TADA {model_size} model...")
+            snapshot_download(
+                repo_id=repo,
+                token=None,
+                allow_patterns=["*.safetensors", "*.json", "*.txt", "*.bin", "*.model"],
+            )
+
+            # TADA hardcodes "meta-llama/Llama-3.2-1B" as the tokenizer
+            # source in its Aligner and TadaForCausalLM.from_pretrained().
+            # That repo is gated (requires Meta license acceptance).
+            # Download the tokenizer from an ungated mirror and get its
+            # local cache path so we can point TADA at it directly.
+            logger.info("Downloading Llama tokenizer (ungated mirror)...")
+            tokenizer_path = snapshot_download(
+                repo_id="unsloth/Llama-3.2-1B",
+                token=None,
+                allow_patterns=["tokenizer*", "special_tokens*"],
+            )
+
+            # Determine dtype — use bf16 on CUDA/XPU for ~50% memory savings
+            if device == "cuda" and torch.cuda.is_bf16_supported():
+                model_dtype = torch.bfloat16
+            elif device == "xpu":
+                # Intel Arc (Alchemist+) supports bf16 natively
+                model_dtype = torch.bfloat16
+            else:
+                model_dtype = torch.float32
+
+            # Patch the Aligner config class to use the local tokenizer
+            # path instead of the gated "meta-llama/Llama-3.2-1B" default.
+            # This avoids monkey-patching AutoTokenizer.from_pretrained
+            # which corrupts the classmethod descriptor for other engines.
+            from tada.modules.aligner import AlignerConfig
+
+            AlignerConfig.tokenizer_name = tokenizer_path
+
+            # Load encoder (only needed for voice prompt encoding)
+            from tada.modules.encoder import Encoder
+
+            logger.info("Loading TADA encoder...")
+            self.encoder = Encoder.from_pretrained(TADA_CODEC_REPO, subfolder="encoder").to(device)
+            self.encoder.eval()
+
+            # Load the causal LM (includes decoder for wav generation).
+            # TadaForCausalLM.from_pretrained() calls
+            #   getattr(config, "tokenizer_name", "meta-llama/Llama-3.2-1B")
+            # which hits the gated repo. Pre-load the config from HF,
+            # inject the local tokenizer path, then pass it in.
+            from tada.modules.tada import TadaForCausalLM, TadaConfig
+
+            logger.info(f"Loading TADA {model_size} model...")
+            config = TadaConfig.from_pretrained(repo)
+            config.tokenizer_name = tokenizer_path
+            self.model = TadaForCausalLM.from_pretrained(repo, config=config, torch_dtype=model_dtype).to(device)
+            self.model.eval()
+
+        logger.info(f"HumeAI TADA {model_size} loaded successfully on {device}")
+
+    def unload_model(self) -> None:
+        """Unload model and encoder to free memory."""
+        if self.model is not None:
+            del self.model
+            self.model = None
+        if self.encoder is not None:
+            del self.encoder
+            self.encoder = None
+
+        device = self._device
+        self._device = None
+
+        if device:
+            empty_device_cache(device)
+
+        logger.info("HumeAI TADA unloaded")
+
+    async def create_voice_prompt(
+        self,
+        audio_path: str,
+        reference_text: str,
+        use_cache: bool = True,
+    ) -> Tuple[dict, bool]:
+        """
+        Create voice prompt from reference audio using TADA's encoder.
+
+        TADA's encoder performs forced alignment between audio and text tokens,
+        producing an EncoderOutput with 1:1 token-audio alignment. If no
+        reference_text is provided, the encoder uses built-in ASR (English only).
+
+        We serialize the EncoderOutput to a dict for caching.
+        """
+        await self.load_model(self.model_size)
+
+        cache_key = ("tada_" + get_cache_key(audio_path, reference_text)) if use_cache else None
+
+        if cache_key:
+            cached = get_cached_voice_prompt(cache_key)
+            if cached is not None and isinstance(cached, dict):
+                return cached, True
+
+        def _encode_sync():
+            import torch
+            import soundfile as sf
+
+            device = self._device
+
+            # Load audio with soundfile (torchaudio 2.10+ requires torchcodec)
+            audio_np, sr = sf.read(str(audio_path), dtype="float32")
+            audio = torch.from_numpy(audio_np).float()
+            if audio.ndim == 1:
+                audio = audio.unsqueeze(0)  # (samples,) -> (1, samples)
+            else:
+                audio = audio.T  # (samples, channels) -> (channels, samples)
+            audio = audio.to(device)
+
+            # Encode with forced alignment
+            text_arg = [reference_text] if reference_text else None
+            prompt = self.encoder(audio, text=text_arg, sample_rate=sr)
+
+            # Serialize EncoderOutput to a dict of CPU tensors for caching
+            prompt_dict = {}
+            for field_name in prompt.__dataclass_fields__:
+                val = getattr(prompt, field_name)
+                if isinstance(val, torch.Tensor):
+                    prompt_dict[field_name] = val.detach().cpu()
+                elif isinstance(val, list):
+                    prompt_dict[field_name] = val
+                elif isinstance(val, (int, float)):
+                    prompt_dict[field_name] = val
+                else:
+                    prompt_dict[field_name] = val
+            return prompt_dict
+
+        encoded = await asyncio.to_thread(_encode_sync)
+
+        if cache_key:
+            cache_voice_prompt(cache_key, encoded)
+
+        return encoded, False
+
+    async def combine_voice_prompts(
+        self,
+        audio_paths: List[str],
+        reference_texts: List[str],
+    ) -> Tuple[np.ndarray, str]:
+        return await _combine_voice_prompts(audio_paths, reference_texts, sample_rate=24000)
+
+    async def generate(
+        self,
+        text: str,
+        voice_prompt: dict,
+        language: str = "en",
+        seed: Optional[int] = None,
+        instruct: Optional[str] = None,
+    ) -> Tuple[np.ndarray, int]:
+        """
+        Generate audio from text using HumeAI TADA.
+
+        Args:
+            text: Text to synthesize
+            voice_prompt: Serialized EncoderOutput dict from create_voice_prompt()
+            language: Language code (en, ar, de, es, fr, it, ja, pl, pt, zh)
+            seed: Random seed for reproducibility
+            instruct: Not supported by TADA (ignored)
+
+        Returns:
+            Tuple of (audio_array, sample_rate=24000)
+        """
+        await self.load_model(self.model_size)
+
+        def _generate_sync():
+            import torch
+            from tada.modules.encoder import EncoderOutput
+
+            if seed is not None:
+                manual_seed(seed, self._device)
+
+            device = self._device
+
+            # Reconstruct EncoderOutput from the cached dict
+            restored = {}
+            for k, v in voice_prompt.items():
+                if isinstance(v, torch.Tensor):
+                    # Move to device and match model dtype for float tensors
+                    if v.is_floating_point():
+                        model_dtype = next(self.model.parameters()).dtype
+                        restored[k] = v.to(device=device, dtype=model_dtype)
+                    else:
+                        restored[k] = v.to(device=device)
+                else:
+                    restored[k] = v
+
+            prompt = EncoderOutput(**restored)
+
+            # For non-English with the 3B-ML model, we could reload the
+            # encoder with the language-specific aligner. However, the
+            # generation itself is language-agnostic — only the encoder's
+            # aligner changes. Since we encode at create_voice_prompt time,
+            # the language is already baked in. For simplicity, we don't
+            # reload the encoder here.
+
+            logger.info(f"[TADA] Generating ({language}), text length: {len(text)}")
+
+            output = self.model.generate(
+                prompt=prompt,
+                text=text,
+            )
+
+            # output.audio is a list of tensors (one per batch item)
+            if output.audio and output.audio[0] is not None:
+                audio_tensor = output.audio[0]
+                audio = audio_tensor.detach().cpu().numpy().squeeze().astype(np.float32)
+            else:
+                logger.warning("[TADA] Generation produced no audio")
+                audio = np.zeros(24000, dtype=np.float32)
+
+            return audio, 24000
+
+        return await asyncio.to_thread(_generate_sync)

backend/backends/kokoro_backend.py

@@ -0,0 +1,288 @@
+"""
+Kokoro TTS backend implementation.
+
+Wraps the Kokoro-82M model for fast, lightweight text-to-speech.
+82M parameters, CPU realtime, 24kHz output, Apache 2.0 license.
+
+Kokoro uses pre-built voice style vectors (not traditional zero-shot cloning
+from arbitrary audio). Voice prompts are stored as deferred references to
+HF-hosted voice .pt files.
+
+Languages supported (via misaki G2P):
+  - American English (a), British English (b)
+  - Spanish (e), French (f), Hindi (h), Italian (i), Portuguese (p)
+  - Japanese (j) — requires misaki[ja]
+  - Chinese (z) — requires misaki[zh]
+"""
+
+import asyncio
+import logging
+import os
+from typing import Optional
+
+import numpy as np
+
+from . import TTSBackend
+from .base import (
+    get_torch_device,
+    combine_voice_prompts as _combine_voice_prompts,
+    model_load_progress,
+)
+
+logger = logging.getLogger(__name__)
+
+# HuggingFace repo for model + voice detection
+KOKORO_HF_REPO = "hexgrad/Kokoro-82M"
+KOKORO_SAMPLE_RATE = 24000
+
+# Default voice if none specified
+KOKORO_DEFAULT_VOICE = "af_heart"
+
+# All available Kokoro voices: (voice_id, display_name, gender, lang_code)
+KOKORO_VOICES = [
+    # American English female
+    ("af_alloy", "Alloy", "female", "en"),
+    ("af_aoede", "Aoede", "female", "en"),
+    ("af_bella", "Bella", "female", "en"),
+    ("af_heart", "Heart", "female", "en"),
+    ("af_jessica", "Jessica", "female", "en"),
+    ("af_kore", "Kore", "female", "en"),
+    ("af_nicole", "Nicole", "female", "en"),
+    ("af_nova", "Nova", "female", "en"),
+    ("af_river", "River", "female", "en"),
+    ("af_sarah", "Sarah", "female", "en"),
+    ("af_sky", "Sky", "female", "en"),
+    # American English male
+    ("am_adam", "Adam", "male", "en"),
+    ("am_echo", "Echo", "male", "en"),
+    ("am_eric", "Eric", "male", "en"),
+    ("am_fenrir", "Fenrir", "male", "en"),
+    ("am_liam", "Liam", "male", "en"),
+    ("am_michael", "Michael", "male", "en"),
+    ("am_onyx", "Onyx", "male", "en"),
+    ("am_puck", "Puck", "male", "en"),
+    ("am_santa", "Santa", "male", "en"),
+    # British English female
+    ("bf_alice", "Alice", "female", "en"),
+    ("bf_emma", "Emma", "female", "en"),
+    ("bf_isabella", "Isabella", "female", "en"),
+    ("bf_lily", "Lily", "female", "en"),
+    # British English male
+    ("bm_daniel", "Daniel", "male", "en"),
+    ("bm_fable", "Fable", "male", "en"),
+    ("bm_george", "George", "male", "en"),
+    ("bm_lewis", "Lewis", "male", "en"),
+    # Spanish
+    ("ef_dora", "Dora", "female", "es"),
+    ("em_alex", "Alex", "male", "es"),
+    ("em_santa", "Santa", "male", "es"),
+    # French
+    ("ff_siwis", "Siwis", "female", "fr"),
+    # Hindi
+    ("hf_alpha", "Alpha", "female", "hi"),
+    ("hf_beta", "Beta", "female", "hi"),
+    ("hm_omega", "Omega", "male", "hi"),
+    ("hm_psi", "Psi", "male", "hi"),
+    # Italian
+    ("if_sara", "Sara", "female", "it"),
+    ("im_nicola", "Nicola", "male", "it"),
+    # Japanese
+    ("jf_alpha", "Alpha", "female", "ja"),
+    ("jf_gongitsune", "Gongitsune", "female", "ja"),
+    ("jf_nezumi", "Nezumi", "female", "ja"),
+    ("jf_tebukuro", "Tebukuro", "female", "ja"),
+    ("jm_kumo", "Kumo", "male", "ja"),
+    # Portuguese
+    ("pf_dora", "Dora", "female", "pt"),
+    ("pm_alex", "Alex", "male", "pt"),
+    ("pm_santa", "Santa", "male", "pt"),
+    # Chinese
+    ("zf_xiaobei", "Xiaobei", "female", "zh"),
+    ("zf_xiaoni", "Xiaoni", "female", "zh"),
+    ("zf_xiaoxiao", "Xiaoxiao", "female", "zh"),
+    ("zf_xiaoyi", "Xiaoyi", "female", "zh"),
+]
+
+# Map our ISO language codes to Kokoro lang_code characters
+LANG_CODE_MAP = {
+    "en": "a",  # American English
+    "es": "e",
+    "fr": "f",
+    "hi": "h",
+    "it": "i",
+    "pt": "p",
+    "ja": "j",
+    "zh": "z",
+}
+
+
+class KokoroTTSBackend:
+    """Kokoro-82M TTS backend — tiny, fast, CPU-friendly."""
+
+    def __init__(self):
+        self._model = None
+        self._pipelines: dict = {}  # lang_code -> KPipeline
+        self._device: Optional[str] = None
+        self.model_size = "default"
+
+    def _get_device(self) -> str:
+        """Select device. Kokoro supports CUDA and CPU. MPS needs fallback env var."""
+        device = get_torch_device(allow_mps=False)
+        # Kokoro can use MPS but requires PYTORCH_ENABLE_MPS_FALLBACK=1
+        # For now, skip MPS to avoid user confusion — CPU is already realtime
+        return device
+
+    @property
+    def device(self) -> str:
+        if self._device is None:
+            self._device = self._get_device()
+        return self._device
+
+    def is_loaded(self) -> bool:
+        return self._model is not None
+
+    def _get_model_path(self, model_size: str) -> str:
+        return KOKORO_HF_REPO
+
+    def _is_model_cached(self, model_size: str = "default") -> bool:
+        """Check if Kokoro model files are cached locally."""
+        from .base import is_model_cached
+
+        return is_model_cached(
+            KOKORO_HF_REPO,
+            required_files=["config.json", "kokoro-v1_0.pth"],
+        )
+
+    async def load_model(self, model_size: str = "default") -> None:
+        """Load the Kokoro model."""
+        if self._model is not None:
+            return
+        await asyncio.to_thread(self._load_model_sync)
+
+    def _load_model_sync(self):
+        """Synchronous model loading."""
+        model_name = "kokoro"
+        is_cached = self._is_model_cached()
+
+        with model_load_progress(model_name, is_cached):
+            from kokoro import KModel
+
+            device = self.device
+            logger.info(f"Loading Kokoro-82M on {device}...")
+
+            self._model = KModel(repo_id=KOKORO_HF_REPO).to(device).eval()
+
+        logger.info("Kokoro-82M loaded successfully")
+
+    def _get_pipeline(self, lang_code: str):
+        """Get or create a KPipeline for the given language code."""
+        kokoro_lang = LANG_CODE_MAP.get(lang_code, "a")
+
+        if kokoro_lang not in self._pipelines:
+            from kokoro import KPipeline
+
+            # Create pipeline with our existing model (no redundant model loading)
+            self._pipelines[kokoro_lang] = KPipeline(
+                lang_code=kokoro_lang,
+                repo_id=KOKORO_HF_REPO,
+                model=self._model,
+            )
+
+        return self._pipelines[kokoro_lang]
+
+    def unload_model(self) -> None:
+        """Unload model to free memory."""
+        if self._model is not None:
+            del self._model
+            self._model = None
+            self._pipelines.clear()
+
+            import torch
+
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+
+            logger.info("Kokoro unloaded")
+
+    async def create_voice_prompt(
+        self,
+        audio_path: str,
+        reference_text: str,
+        use_cache: bool = True,
+    ) -> tuple[dict, bool]:
+        """
+        Create voice prompt for Kokoro.
+
+        Kokoro doesn't do traditional voice cloning from arbitrary audio.
+        When called for a cloned profile (fallback), uses the default voice.
+        For preset profiles, the voice_prompt dict is built by the profile
+        service and bypasses this method entirely.
+        """
+        return {
+            "voice_type": "preset",
+            "preset_engine": "kokoro",
+            "preset_voice_id": KOKORO_DEFAULT_VOICE,
+        }, False
+
+    async def combine_voice_prompts(
+        self,
+        audio_paths: list[str],
+        reference_texts: list[str],
+    ) -> tuple[np.ndarray, str]:
+        """Combine voice prompts — uses base implementation for audio concatenation."""
+        return await _combine_voice_prompts(
+            audio_paths, reference_texts, sample_rate=KOKORO_SAMPLE_RATE
+        )
+
+    async def generate(
+        self,
+        text: str,
+        voice_prompt: dict,
+        language: str = "en",
+        seed: Optional[int] = None,
+        instruct: Optional[str] = None,
+    ) -> tuple[np.ndarray, int]:
+        """
+        Generate audio from text using Kokoro.
+
+        Args:
+            text: Text to synthesize
+            voice_prompt: Dict with kokoro_voice key
+            language: Language code
+            seed: Random seed for reproducibility
+            instruct: Not supported by Kokoro (ignored)
+
+        Returns:
+            Tuple of (audio_array, sample_rate)
+        """
+        await self.load_model()
+
+        voice_name = voice_prompt.get("preset_voice_id") or voice_prompt.get("kokoro_voice") or KOKORO_DEFAULT_VOICE
+
+        def _generate_sync():
+            import torch
+
+            if seed is not None:
+                torch.manual_seed(seed)
+                if torch.cuda.is_available():
+                    torch.cuda.manual_seed(seed)
+
+            pipeline = self._get_pipeline(language)
+
+            # Generate all chunks and concatenate
+            audio_chunks = []
+            for result in pipeline(text, voice=voice_name, speed=1.0):
+                if result.audio is not None:
+                    chunk = result.audio
+                    if isinstance(chunk, torch.Tensor):
+                        chunk = chunk.detach().cpu().numpy()
+                    audio_chunks.append(chunk.squeeze())
+
+            if not audio_chunks:
+                # Return 1 second of silence as fallback
+                return np.zeros(KOKORO_SAMPLE_RATE, dtype=np.float32), KOKORO_SAMPLE_RATE
+
+            audio = np.concatenate(audio_chunks)
+            return audio.astype(np.float32), KOKORO_SAMPLE_RATE
+
+        return await asyncio.to_thread(_generate_sync)

backend/backends/luxtts_backend.py

@@ -0,0 +1,184 @@
+"""
+LuxTTS backend implementation.
+
+Wraps the LuxTTS (ZipVoice) model for zero-shot voice cloning.
+~1GB VRAM, 48kHz output, 150x realtime on CPU.
+"""
+
+import asyncio
+import logging
+from typing import Optional, Tuple
+
+import numpy as np
+
+from . import TTSBackend
+from .base import (
+    is_model_cached,
+    get_torch_device,
+    empty_device_cache,
+    manual_seed,
+    combine_voice_prompts as _combine_voice_prompts,
+    model_load_progress,
+)
+from ..utils.cache import get_cache_key, get_cached_voice_prompt, cache_voice_prompt
+
+logger = logging.getLogger(__name__)
+
+# HuggingFace repo for model weight detection
+LUXTTS_HF_REPO = "YatharthS/LuxTTS"
+
+
+class LuxTTSBackend:
+    """LuxTTS backend for zero-shot voice cloning."""
+
+    def __init__(self):
+        self.model = None
+        self.model_size = "default"  # LuxTTS has only one model size
+        self._device = None
+
+    def _get_device(self) -> str:
+        return get_torch_device(allow_mps=True, allow_xpu=True)
+
+    def is_loaded(self) -> bool:
+        return self.model is not None
+
+    @property
+    def device(self) -> str:
+        if self._device is None:
+            self._device = self._get_device()
+        return self._device
+
+    def _get_model_path(self, model_size: str) -> str:
+        return LUXTTS_HF_REPO
+
+    def _is_model_cached(self, model_size: str = "default") -> bool:
+        return is_model_cached(
+            LUXTTS_HF_REPO,
+            weight_extensions=(".pt", ".safetensors", ".onnx", ".bin"),
+        )
+
+    async def load_model(self, model_size: str = "default") -> None:
+        """Load the LuxTTS model."""
+        if self.model is not None:
+            return
+
+        await asyncio.to_thread(self._load_model_sync)
+
+    def _load_model_sync(self):
+        model_name = "luxtts"
+        is_cached = self._is_model_cached()
+
+        with model_load_progress(model_name, is_cached):
+            from zipvoice.luxvoice import LuxTTS
+
+            device = self.device
+            logger.info(f"Loading LuxTTS on {device}...")
+
+            if device == "cpu":
+                import os
+
+                threads = os.cpu_count() or 4
+                self.model = LuxTTS(
+                    model_path=LUXTTS_HF_REPO,
+                    device="cpu",
+                    threads=min(threads, 8),
+                )
+            else:
+                self.model = LuxTTS(model_path=LUXTTS_HF_REPO, device=device)
+
+        logger.info("LuxTTS loaded successfully")
+
+    def unload_model(self) -> None:
+        """Unload model to free memory."""
+        if self.model is not None:
+            device = self.device
+            del self.model
+            self.model = None
+            self._device = None
+
+            empty_device_cache(device)
+
+            logger.info("LuxTTS unloaded")
+
+    async def create_voice_prompt(
+        self,
+        audio_path: str,
+        reference_text: str,
+        use_cache: bool = True,
+    ) -> Tuple[dict, bool]:
+        """
+        Create voice prompt from reference audio.
+
+        LuxTTS uses its own encode_prompt() which runs Whisper ASR internally
+        to transcribe the reference. The reference_text parameter is not used
+        by LuxTTS itself, but we include it in the cache key for consistency.
+        """
+        await self.load_model()
+
+        # Compute cache key once for both lookup and storage
+        cache_key = ("luxtts_" + get_cache_key(audio_path, reference_text)) if use_cache else None
+
+        if cache_key:
+            cached = get_cached_voice_prompt(cache_key)
+            if cached is not None and isinstance(cached, dict):
+                return cached, True
+
+        def _encode_sync():
+            return self.model.encode_prompt(
+                prompt_audio=str(audio_path),
+                duration=5,
+                rms=0.01,
+            )
+
+        encoded = await asyncio.to_thread(_encode_sync)
+
+        if cache_key:
+            cache_voice_prompt(cache_key, encoded)
+
+        return encoded, False
+
+    async def combine_voice_prompts(self, audio_paths, reference_texts):
+        return await _combine_voice_prompts(audio_paths, reference_texts, sample_rate=24000)
+
+    async def generate(
+        self,
+        text: str,
+        voice_prompt: dict,
+        language: str = "en",
+        seed: Optional[int] = None,
+        instruct: Optional[str] = None,
+    ) -> Tuple[np.ndarray, int]:
+        """
+        Generate audio from text using LuxTTS.
+
+        Args:
+            text: Text to synthesize
+            voice_prompt: Encoded prompt dict from encode_prompt()
+            language: Language code (LuxTTS is English-focused)
+            seed: Random seed for reproducibility
+            instruct: Not supported by LuxTTS (ignored)
+
+        Returns:
+            Tuple of (audio_array, sample_rate)
+        """
+        await self.load_model()
+
+        def _generate_sync():
+            if seed is not None:
+                manual_seed(seed, self.device)
+
+            wav = self.model.generate_speech(
+                text=text,
+                encode_dict=voice_prompt,
+                num_steps=4,
+                guidance_scale=3.0,
+                t_shift=0.5,
+                speed=1.0,
+                return_smooth=False,  # 48kHz output
+            )
+
+            # LuxTTS returns a tensor (may be on GPU/MPS), move to CPU first
+            audio = wav.detach().cpu().numpy().squeeze()
+            return audio, 48000
+
+        return await asyncio.to_thread(_generate_sync)

backend/backends/mlx_backend.py

@@ -0,0 +1,367 @@
+"""
+MLX backend implementation for TTS and STT using mlx-audio.
+"""
+
+from typing import Optional, List, Tuple
+import asyncio
+import logging
+import numpy as np
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+# PATCH: Import and apply offline patch BEFORE any huggingface_hub usage
+# This prevents mlx_audio from making network requests when models are cached
+from ..utils.hf_offline_patch import patch_huggingface_hub_offline, ensure_original_qwen_config_cached
+
+patch_huggingface_hub_offline()
+ensure_original_qwen_config_cached()
+
+from . import TTSBackend, STTBackend, LANGUAGE_CODE_TO_NAME, WHISPER_HF_REPOS
+from .base import is_model_cached, combine_voice_prompts as _combine_voice_prompts, model_load_progress
+from ..utils.cache import get_cache_key, get_cached_voice_prompt, cache_voice_prompt
+
+
+class MLXTTSBackend:
+    """MLX-based TTS backend using mlx-audio."""
+
+    def __init__(self, model_size: str = "1.7B"):
+        self.model = None
+        self.model_size = model_size
+        self._current_model_size = None
+
+    def is_loaded(self) -> bool:
+        """Check if model is loaded."""
+        return self.model is not None
+
+    def _get_model_path(self, model_size: str) -> str:
+        """
+        Get the MLX model path.
+
+        Args:
+            model_size: Model size (1.7B or 0.6B)
+
+        Returns:
+            HuggingFace Hub model ID for MLX
+        """
+        mlx_model_map = {
+            "1.7B": "mlx-community/Qwen3-TTS-12Hz-1.7B-Base-bf16",
+            "0.6B": "mlx-community/Qwen3-TTS-12Hz-0.6B-Base-bf16",
+        }
+
+        if model_size not in mlx_model_map:
+            raise ValueError(f"Unknown model size: {model_size}")
+
+        hf_model_id = mlx_model_map[model_size]
+        logger.info("Will download MLX model from HuggingFace Hub: %s", hf_model_id)
+
+        return hf_model_id
+
+    def _is_model_cached(self, model_size: str) -> bool:
+        return is_model_cached(
+            self._get_model_path(model_size),
+            weight_extensions=(".safetensors", ".bin", ".npz"),
+        )
+
+    async def load_model_async(self, model_size: Optional[str] = None):
+        """
+        Lazy load the MLX TTS model.
+
+        Args:
+            model_size: Model size to load (1.7B or 0.6B)
+        """
+        if model_size is None:
+            model_size = self.model_size
+
+        # If already loaded with correct size, return
+        if self.model is not None and self._current_model_size == model_size:
+            return
+
+        # Unload existing model if different size requested
+        if self.model is not None and self._current_model_size != model_size:
+            self.unload_model()
+
+        # Run blocking load in thread pool
+        await asyncio.to_thread(self._load_model_sync, model_size)
+
+    # Alias for compatibility
+    load_model = load_model_async
+
+    def _load_model_sync(self, model_size: str):
+        """Synchronous model loading."""
+        model_path = self._get_model_path(model_size)
+        model_name = f"qwen-tts-{model_size}"
+        is_cached = self._is_model_cached(model_size)
+
+        with model_load_progress(model_name, is_cached):
+            from mlx_audio.tts import load
+
+            logger.info("Loading MLX TTS model %s...", model_size)
+
+            self.model = load(model_path)
+
+        self._current_model_size = model_size
+        self.model_size = model_size
+        logger.info("MLX TTS model %s loaded successfully", model_size)
+
+    def unload_model(self):
+        """Unload the model to free memory."""
+        if self.model is not None:
+            del self.model
+            self.model = None
+            self._current_model_size = None
+            logger.info("MLX TTS model unloaded")
+
+    async def create_voice_prompt(
+        self,
+        audio_path: str,
+        reference_text: str,
+        use_cache: bool = True,
+    ) -> Tuple[dict, bool]:
+        """
+        Create voice prompt from reference audio.
+
+        MLX backend stores voice prompt as a dict with audio path and text.
+        The actual voice prompt processing happens during generation.
+
+        Args:
+            audio_path: Path to reference audio file
+            reference_text: Transcript of reference audio
+            use_cache: Whether to use cached prompt if available
+
+        Returns:
+            Tuple of (voice_prompt_dict, was_cached)
+        """
+        await self.load_model_async(None)
+
+        # Check cache if enabled
+        if use_cache:
+            cache_key = get_cache_key(audio_path, reference_text)
+            cached_prompt = get_cached_voice_prompt(cache_key)
+            if cached_prompt is not None:
+                # Return cached prompt (should be dict format)
+                if isinstance(cached_prompt, dict):
+                    # Validate that the cached audio file still exists
+                    cached_audio_path = cached_prompt.get("ref_audio") or cached_prompt.get("ref_audio_path")
+                    if cached_audio_path and Path(cached_audio_path).exists():
+                        return cached_prompt, True
+                    else:
+                        # Cached file no longer exists, invalidate cache
+                        logger.warning("Cached audio file not found: %s, regenerating prompt", cached_audio_path)
+
+        # MLX voice prompt format - store audio path and text
+        # The model will process this during generation
+        voice_prompt_items = {
+            "ref_audio": str(audio_path),
+            "ref_text": reference_text,
+        }
+
+        # Cache if enabled
+        if use_cache:
+            cache_key = get_cache_key(audio_path, reference_text)
+            cache_voice_prompt(cache_key, voice_prompt_items)
+
+        return voice_prompt_items, False
+
+    async def combine_voice_prompts(self, audio_paths, reference_texts):
+        return await _combine_voice_prompts(audio_paths, reference_texts)
+
+    async def generate(
+        self,
+        text: str,
+        voice_prompt: dict,
+        language: str = "en",
+        seed: Optional[int] = None,
+        instruct: Optional[str] = None,
+    ) -> Tuple[np.ndarray, int]:
+        """
+        Generate audio from text using voice prompt.
+
+        Args:
+            text: Text to synthesize
+            voice_prompt: Voice prompt dictionary with ref_audio and ref_text
+            language: Language code (en or zh) - may not be fully supported by MLX
+            seed: Random seed for reproducibility
+            instruct: Natural language instruction (may not be supported by MLX)
+
+        Returns:
+            Tuple of (audio_array, sample_rate)
+        """
+        await self.load_model_async(None)
+
+        logger.info("Generating audio for text: %s", text)
+
+        def _generate_sync():
+            """Run synchronous generation in thread pool."""
+            # MLX generate() returns a generator yielding GenerationResult objects
+            audio_chunks = []
+            sample_rate = 24000
+            lang = LANGUAGE_CODE_TO_NAME.get(language, "auto")
+
+            # Set seed if provided (MLX uses numpy random)
+            if seed is not None:
+                import mlx.core as mx
+
+                np.random.seed(seed)
+                mx.random.seed(seed)
+
+            # Extract voice prompt info
+            ref_audio = voice_prompt.get("ref_audio") or voice_prompt.get("ref_audio_path")
+            ref_text = voice_prompt.get("ref_text", "")
+
+            # Validate that the audio file exists
+            if ref_audio and not Path(ref_audio).exists():
+                logger.warning("Audio file not found: %s", ref_audio)
+                logger.warning("This may be due to a cached voice prompt referencing a deleted temp file.")
+                logger.warning("Regenerating without voice prompt.")
+                ref_audio = None
+
+            # Inference runs with the process's default HF_HUB_OFFLINE
+            # state. Forcing offline here (previously used to avoid lazy
+            # mlx_audio lookups hanging when the network drops mid-inference,
+            # issue #462) regressed online users because libraries make
+            # legitimate metadata calls during generation.
+            try:
+                if ref_audio:
+                    # Check if generate accepts ref_audio parameter
+                    import inspect
+
+                    sig = inspect.signature(self.model.generate)
+                    if "ref_audio" in sig.parameters:
+                        # Generate with voice cloning
+                        for result in self.model.generate(text, ref_audio=ref_audio, ref_text=ref_text, lang_code=lang):
+                            audio_chunks.append(np.array(result.audio))
+                            sample_rate = result.sample_rate
+                    else:
+                        # Fallback: generate without voice cloning
+                        for result in self.model.generate(text, lang_code=lang):
+                            audio_chunks.append(np.array(result.audio))
+                            sample_rate = result.sample_rate
+                else:
+                    # No voice prompt, generate normally
+                    for result in self.model.generate(text, lang_code=lang):
+                        audio_chunks.append(np.array(result.audio))
+                        sample_rate = result.sample_rate
+            except Exception as e:
+                # If voice cloning fails, try without it
+                logger.warning("Voice cloning failed, generating without voice prompt: %s", e)
+                for result in self.model.generate(text, lang_code=lang):
+                    audio_chunks.append(np.array(result.audio))
+                    sample_rate = result.sample_rate
+
+            # Concatenate all chunks
+            if audio_chunks:
+                audio = np.concatenate([np.asarray(chunk, dtype=np.float32) for chunk in audio_chunks])
+            else:
+                # Fallback: empty audio
+                audio = np.array([], dtype=np.float32)
+
+            return audio, sample_rate
+
+        # Run blocking inference in thread pool
+        audio, sample_rate = await asyncio.to_thread(_generate_sync)
+
+        return audio, sample_rate
+
+
+class MLXSTTBackend:
+    """MLX-based STT backend using mlx-audio Whisper."""
+
+    def __init__(self, model_size: str = "base"):
+        self.model = None
+        self.model_size = model_size
+
+    def is_loaded(self) -> bool:
+        """Check if model is loaded."""
+        return self.model is not None
+
+    def _is_model_cached(self, model_size: str) -> bool:
+        hf_repo = WHISPER_HF_REPOS.get(model_size, f"openai/whisper-{model_size}")
+        return is_model_cached(hf_repo, weight_extensions=(".safetensors", ".bin", ".npz"))
+
+    async def load_model_async(self, model_size: Optional[str] = None):
+        """
+        Lazy load the MLX Whisper model.
+
+        Args:
+            model_size: Model size (tiny, base, small, medium, large)
+        """
+        if model_size is None:
+            model_size = self.model_size
+
+        if self.model is not None and self.model_size == model_size:
+            return
+
+        # Run blocking load in thread pool
+        await asyncio.to_thread(self._load_model_sync, model_size)
+
+    # Alias for compatibility
+    load_model = load_model_async
+
+    def _load_model_sync(self, model_size: str):
+        """Synchronous model loading."""
+        progress_model_name = f"whisper-{model_size}"
+        is_cached = self._is_model_cached(model_size)
+
+        with model_load_progress(progress_model_name, is_cached):
+            from mlx_audio.stt import load
+
+            model_name = WHISPER_HF_REPOS.get(model_size, f"openai/whisper-{model_size}")
+            logger.info("Loading MLX Whisper model %s...", model_size)
+
+            self.model = load(model_name)
+
+        self.model_size = model_size
+        logger.info("MLX Whisper model %s loaded successfully", model_size)
+
+    def unload_model(self):
+        """Unload the model to free memory."""
+        if self.model is not None:
+            del self.model
+            self.model = None
+            logger.info("MLX Whisper model unloaded")
+
+    async def transcribe(
+        self,
+        audio_path: str,
+        language: Optional[str] = None,
+        model_size: Optional[str] = None,
+    ) -> str:
+        """
+        Transcribe audio to text.
+
+        Args:
+            audio_path: Path to audio file
+            language: Optional language hint
+            model_size: Optional model size override
+
+        Returns:
+            Transcribed text
+        """
+        await self.load_model_async(model_size)
+
+        def _transcribe_sync():
+            """Run synchronous transcription in thread pool."""
+            # MLX Whisper transcription using generate method
+            # The generate method accepts audio path directly
+            decode_options = {}
+            if language:
+                decode_options["language"] = language
+
+            # Inference runs with the process's default HF_HUB_OFFLINE
+            # state — see the comment in MLXTTSBackend.generate for the
+            # regression this revert fixes (issue #462).
+            result = self.model.generate(str(audio_path), **decode_options)
+
+            # Extract text from result
+            if isinstance(result, str):
+                return result.strip()
+            elif isinstance(result, dict):
+                return result.get("text", "").strip()
+            elif hasattr(result, "text"):
+                return result.text.strip()
+            else:
+                return str(result).strip()
+
+        # Run blocking transcription in thread pool
+        return await asyncio.to_thread(_transcribe_sync)

backend/backends/pytorch_backend.py

@@ -0,0 +1,378 @@
+"""
+PyTorch backend implementation for TTS and STT.
+"""
+
+from typing import Optional, List, Tuple
+import asyncio
+import logging
+import torch
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+from . import TTSBackend, STTBackend, LANGUAGE_CODE_TO_NAME, WHISPER_HF_REPOS
+from .base import (
+    is_model_cached,
+    get_torch_device,
+    empty_device_cache,
+    manual_seed,
+    combine_voice_prompts as _combine_voice_prompts,
+    model_load_progress,
+)
+from ..utils.cache import get_cache_key, get_cached_voice_prompt, cache_voice_prompt
+from ..utils.audio import load_audio
+
+
+class PyTorchTTSBackend:
+    """PyTorch-based TTS backend using Qwen3-TTS."""
+
+    def __init__(self, model_size: str = "1.7B"):
+        self.model = None
+        self.model_size = model_size
+        self.device = self._get_device()
+        self._current_model_size = None
+
+    def _get_device(self) -> str:
+        """Get the best available device."""
+        return get_torch_device(allow_xpu=True, allow_directml=True)
+
+    def is_loaded(self) -> bool:
+        """Check if model is loaded."""
+        return self.model is not None
+
+    def _get_model_path(self, model_size: str) -> str:
+        """
+        Get the HuggingFace Hub model ID.
+
+        Args:
+            model_size: Model size (1.7B or 0.6B)
+
+        Returns:
+            HuggingFace Hub model ID
+        """
+        hf_model_map = {
+            "1.7B": "Qwen/Qwen3-TTS-12Hz-1.7B-Base",
+            "0.6B": "Qwen/Qwen3-TTS-12Hz-0.6B-Base",
+        }
+
+        if model_size not in hf_model_map:
+            raise ValueError(f"Unknown model size: {model_size}")
+
+        return hf_model_map[model_size]
+
+    def _is_model_cached(self, model_size: str) -> bool:
+        return is_model_cached(self._get_model_path(model_size))
+
+    async def load_model_async(self, model_size: Optional[str] = None):
+        """
+        Lazy load the TTS model with automatic downloading from HuggingFace Hub.
+
+        Args:
+            model_size: Model size to load (1.7B or 0.6B)
+        """
+        if model_size is None:
+            model_size = self.model_size
+
+        # If already loaded with correct size, return
+        if self.model is not None and self._current_model_size == model_size:
+            return
+
+        # Unload existing model if different size requested
+        if self.model is not None and self._current_model_size != model_size:
+            self.unload_model()
+
+        # Run blocking load in thread pool
+        await asyncio.to_thread(self._load_model_sync, model_size)
+
+    # Alias for compatibility
+    load_model = load_model_async
+
+    def _load_model_sync(self, model_size: str):
+        """Synchronous model loading."""
+        model_name = f"qwen-tts-{model_size}"
+        is_cached = self._is_model_cached(model_size)
+
+        with model_load_progress(model_name, is_cached):
+            from qwen_tts import Qwen3TTSModel
+
+            model_path = self._get_model_path(model_size)
+            logger.info("Loading TTS model %s on %s...", model_size, self.device)
+
+            # Route both HF Hub and Transformers through a single cache root.
+            # On Windows local setups, model assets can otherwise split between
+            # .hf-cache/hub and .hf-cache/transformers, causing speech_tokenizer
+            # and preprocessor_config.json to fail to resolve during load.
+            from huggingface_hub import constants as hf_constants
+            tts_cache_dir = hf_constants.HF_HUB_CACHE
+
+            if self.device == "cpu":
+                self.model = Qwen3TTSModel.from_pretrained(
+                    model_path,
+                    cache_dir=tts_cache_dir,
+                    torch_dtype=torch.float32,
+                    low_cpu_mem_usage=False,
+                )
+            else:
+                self.model = Qwen3TTSModel.from_pretrained(
+                    model_path,
+                    cache_dir=tts_cache_dir,
+                    device_map=self.device,
+                    torch_dtype=torch.bfloat16,
+                )
+
+        self._current_model_size = model_size
+        self.model_size = model_size
+        logger.info("TTS model %s loaded successfully", model_size)
+
+    def unload_model(self):
+        """Unload the model to free memory."""
+        if self.model is not None:
+            del self.model
+            self.model = None
+            self._current_model_size = None
+
+            empty_device_cache(self.device)
+
+            logger.info("TTS model unloaded")
+
+    async def create_voice_prompt(
+        self,
+        audio_path: str,
+        reference_text: str,
+        use_cache: bool = True,
+    ) -> Tuple[dict, bool]:
+        """
+        Create voice prompt from reference audio.
+
+        Args:
+            audio_path: Path to reference audio file
+            reference_text: Transcript of reference audio
+            use_cache: Whether to use cached prompt if available
+
+        Returns:
+            Tuple of (voice_prompt_dict, was_cached)
+        """
+        await self.load_model_async(None)
+
+        # Check cache if enabled
+        if use_cache:
+            cache_key = get_cache_key(audio_path, reference_text)
+            cached_prompt = get_cached_voice_prompt(cache_key)
+            if cached_prompt is not None:
+                # Cache stores as torch.Tensor but actual prompt is dict
+                # Convert if needed
+                if isinstance(cached_prompt, dict):
+                    # For PyTorch backend, the dict should contain tensors, not file paths
+                    # So we can safely return it
+                    return cached_prompt, True
+                elif isinstance(cached_prompt, torch.Tensor):
+                    # Legacy cache format - convert to dict
+                    # This shouldn't happen in practice, but handle it
+                    return {"prompt": cached_prompt}, True
+
+        def _create_prompt_sync():
+            """Run synchronous voice prompt creation in thread pool."""
+            # Inference runs with the process's default HF_HUB_OFFLINE
+            # state. Forcing offline here (issue #462) regressed online
+            # users whose libraries issue legitimate metadata lookups
+            # during voice-prompt creation.
+            return self.model.create_voice_clone_prompt(
+                ref_audio=str(audio_path),
+                ref_text=reference_text,
+                x_vector_only_mode=False,
+            )
+
+        # Run blocking operation in thread pool
+        voice_prompt_items = await asyncio.to_thread(_create_prompt_sync)
+
+        # Cache if enabled
+        if use_cache:
+            cache_key = get_cache_key(audio_path, reference_text)
+            cache_voice_prompt(cache_key, voice_prompt_items)
+
+        return voice_prompt_items, False
+
+    async def combine_voice_prompts(
+        self,
+        audio_paths: List[str],
+        reference_texts: List[str],
+    ) -> Tuple[np.ndarray, str]:
+        return await _combine_voice_prompts(audio_paths, reference_texts)
+
+    async def generate(
+        self,
+        text: str,
+        voice_prompt: dict,
+        language: str = "en",
+        seed: Optional[int] = None,
+        instruct: Optional[str] = None,
+    ) -> Tuple[np.ndarray, int]:
+        """
+        Generate audio from text using voice prompt.
+
+        Args:
+            text: Text to synthesize
+            voice_prompt: Voice prompt dictionary from create_voice_prompt
+            language: Language code (en or zh)
+            seed: Random seed for reproducibility
+            instruct: Natural language instruction for speech delivery control
+
+        Returns:
+            Tuple of (audio_array, sample_rate)
+        """
+        # Load model
+        await self.load_model_async(None)
+
+        def _generate_sync():
+            """Run synchronous generation in thread pool."""
+            # Set seed if provided
+            if seed is not None:
+                manual_seed(seed, self.device)
+
+            # See _create_prompt_sync comment — inference runs with the
+            # process's default HF_HUB_OFFLINE state (issue #462).
+            wavs, sample_rate = self.model.generate_voice_clone(
+                text=text,
+                voice_clone_prompt=voice_prompt,
+                language=LANGUAGE_CODE_TO_NAME.get(language, "auto"),
+                instruct=instruct,
+            )
+            return wavs[0], sample_rate
+
+        # Run blocking inference in thread pool to avoid blocking event loop
+        audio, sample_rate = await asyncio.to_thread(_generate_sync)
+
+        return audio, sample_rate
+
+
+class PyTorchSTTBackend:
+    """PyTorch-based STT backend using Whisper."""
+
+    def __init__(self, model_size: str = "base"):
+        self.model = None
+        self.processor = None
+        self.model_size = model_size
+        self.device = self._get_device()
+
+    def _get_device(self) -> str:
+        """Get the best available device."""
+        return get_torch_device(allow_xpu=True, allow_directml=True)
+
+    def is_loaded(self) -> bool:
+        """Check if model is loaded."""
+        return self.model is not None
+
+    def _is_model_cached(self, model_size: str) -> bool:
+        hf_repo = WHISPER_HF_REPOS.get(model_size, f"openai/whisper-{model_size}")
+        return is_model_cached(hf_repo)
+
+    async def load_model_async(self, model_size: Optional[str] = None):
+        """
+        Lazy load the Whisper model.
+
+        Args:
+            model_size: Model size (tiny, base, small, medium, large)
+        """
+        if model_size is None:
+            model_size = self.model_size
+
+        if self.model is not None and self.model_size == model_size:
+            return
+
+        await asyncio.to_thread(self._load_model_sync, model_size)
+
+    # Alias for compatibility
+    load_model = load_model_async
+
+    def _load_model_sync(self, model_size: str):
+        """Synchronous model loading."""
+        progress_model_name = f"whisper-{model_size}"
+        is_cached = self._is_model_cached(model_size)
+
+        with model_load_progress(progress_model_name, is_cached):
+            from transformers import WhisperProcessor, WhisperForConditionalGeneration
+
+            model_name = WHISPER_HF_REPOS.get(model_size, f"openai/whisper-{model_size}")
+            logger.info("Loading Whisper model %s on %s...", model_size, self.device)
+
+            self.processor = WhisperProcessor.from_pretrained(model_name)
+            self.model = WhisperForConditionalGeneration.from_pretrained(model_name)
+
+        self.model.to(self.device)
+        self.model_size = model_size
+        logger.info("Whisper model %s loaded successfully", model_size)
+
+    def unload_model(self):
+        """Unload the model to free memory."""
+        if self.model is not None:
+            del self.model
+            del self.processor
+            self.model = None
+            self.processor = None
+
+            empty_device_cache(self.device)
+
+            logger.info("Whisper model unloaded")
+
+    async def transcribe(
+        self,
+        audio_path: str,
+        language: Optional[str] = None,
+        model_size: Optional[str] = None,
+    ) -> str:
+        """
+        Transcribe audio to text.
+
+        Args:
+            audio_path: Path to audio file
+            language: Optional language hint
+            model_size: Optional model size override
+
+        Returns:
+            Transcribed text
+        """
+        await self.load_model_async(model_size)
+
+        def _transcribe_sync():
+            """Run synchronous transcription in thread pool."""
+            # Load audio
+            audio, _sr = load_audio(audio_path, sample_rate=16000)
+
+            # Inference runs with the process's default HF_HUB_OFFLINE
+            # state — forcing offline here (issue #462) broke online users
+            # whose `get_decoder_prompt_ids` / tokenizer calls issue
+            # legitimate metadata lookups.
+            # Process audio
+            inputs = self.processor(
+                audio,
+                sampling_rate=16000,
+                return_tensors="pt",
+            )
+            inputs = inputs.to(self.device)
+
+            # Generate transcription
+            # If language is provided, force it; otherwise let Whisper auto-detect
+            generate_kwargs = {}
+            if language:
+                forced_decoder_ids = self.processor.get_decoder_prompt_ids(
+                    language=language,
+                    task="transcribe",
+                )
+                generate_kwargs["forced_decoder_ids"] = forced_decoder_ids
+
+            with torch.no_grad():
+                predicted_ids = self.model.generate(
+                    inputs["input_features"],
+                    **generate_kwargs,
+                )
+
+            # Decode
+            transcription = self.processor.batch_decode(
+                predicted_ids,
+                skip_special_tokens=True,
+            )[0]
+
+            return transcription.strip()
+
+        # Run blocking transcription in thread pool
+        return await asyncio.to_thread(_transcribe_sync)

backend/backends/qwen_custom_voice_backend.py

@@ -0,0 +1,214 @@
+"""
+Qwen3-TTS CustomVoice backend implementation.
+
+Wraps the Qwen3-TTS-12Hz CustomVoice model for preset-speaker TTS with
+instruction-based style control. Uses the same qwen_tts library as the
+Base model (pytorch_backend.py) but loads a different checkpoint and
+calls generate_custom_voice() instead of generate_voice_clone().
+
+Key differences from the Base engine:
+  - Uses preset speakers (9 built-in voices) instead of zero-shot cloning
+  - Supports instruct parameter for tone/emotion/prosody control
+  - Two model sizes: 1.7B and 0.6B
+
+Languages supported: zh, en, ja, ko, de, fr, ru, pt, es, it
+"""
+
+import asyncio
+import logging
+from typing import Optional
+
+import numpy as np
+import torch
+
+from . import TTSBackend, LANGUAGE_CODE_TO_NAME
+from .base import (
+    is_model_cached,
+    get_torch_device,
+    combine_voice_prompts as _combine_voice_prompts,
+    model_load_progress,
+)
+
+logger = logging.getLogger(__name__)
+
+# ── Preset speakers ──────────────────────────────────────────────────
+
+# (speaker_id, display_name, gender, native_language_code, description)
+QWEN_CUSTOM_VOICES = [
+    ("Vivian", "Vivian", "female", "zh", "Bright, slightly edgy young female voice"),
+    ("Serena", "Serena", "female", "zh", "Warm, gentle young female voice"),
+    ("Uncle_Fu", "Uncle Fu", "male", "zh", "Seasoned male voice with a low, mellow timbre"),
+    ("Dylan", "Dylan", "male", "zh", "Youthful Beijing male voice with a clear, natural timbre"),
+    ("Eric", "Eric", "male", "zh", "Lively Chengdu male voice with a slightly husky brightness"),
+    ("Ryan", "Ryan", "male", "en", "Dynamic male voice with strong rhythmic drive"),
+    ("Aiden", "Aiden", "male", "en", "Sunny American male voice with a clear midrange"),
+    ("Ono_Anna", "Ono Anna", "female", "ja", "Playful Japanese female voice with a light, nimble timbre"),
+    ("Sohee", "Sohee", "female", "ko", "Warm Korean female voice with rich emotion"),
+]
+
+QWEN_CV_DEFAULT_SPEAKER = "Ryan"
+
+# HuggingFace repo IDs per model size
+QWEN_CV_HF_REPOS = {
+    "1.7B": "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice",
+    "0.6B": "Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice",
+}
+
+
+class QwenCustomVoiceBackend:
+    """Qwen3-TTS CustomVoice backend — preset speakers with instruct control."""
+
+    def __init__(self, model_size: str = "1.7B"):
+        self.model = None
+        self.model_size = model_size
+        self.device = self._get_device()
+        self._current_model_size: Optional[str] = None
+
+    def _get_device(self) -> str:
+        return get_torch_device(allow_xpu=True, allow_directml=True)
+
+    def is_loaded(self) -> bool:
+        return self.model is not None
+
+    def _get_model_path(self, model_size: str) -> str:
+        if model_size not in QWEN_CV_HF_REPOS:
+            raise ValueError(f"Unknown model size: {model_size}")
+        return QWEN_CV_HF_REPOS[model_size]
+
+    def _is_model_cached(self, model_size: Optional[str] = None) -> bool:
+        size = model_size or self.model_size
+        return is_model_cached(self._get_model_path(size))
+
+    async def load_model_async(self, model_size: Optional[str] = None) -> None:
+        if model_size is None:
+            model_size = self.model_size
+
+        if self.model is not None and self._current_model_size == model_size:
+            return
+
+        if self.model is not None and self._current_model_size != model_size:
+            self.unload_model()
+
+        await asyncio.to_thread(self._load_model_sync, model_size)
+
+    # Alias for compatibility with the TTSBackend protocol
+    load_model = load_model_async
+
+    def _load_model_sync(self, model_size: str) -> None:
+        model_name = f"qwen-custom-voice-{model_size}"
+        is_cached = self._is_model_cached(model_size)
+
+        with model_load_progress(model_name, is_cached):
+            from qwen_tts import Qwen3TTSModel
+
+            model_path = self._get_model_path(model_size)
+            logger.info("Loading Qwen CustomVoice %s on %s...", model_size, self.device)
+
+            if self.device == "cpu":
+                self.model = Qwen3TTSModel.from_pretrained(
+                    model_path,
+                    torch_dtype=torch.float32,
+                    low_cpu_mem_usage=False,
+                )
+            else:
+                self.model = Qwen3TTSModel.from_pretrained(
+                    model_path,
+                    device_map=self.device,
+                    torch_dtype=torch.bfloat16,
+                )
+
+        self._current_model_size = model_size
+        self.model_size = model_size
+        logger.info("Qwen CustomVoice %s loaded successfully", model_size)
+
+    def unload_model(self) -> None:
+        if self.model is not None:
+            del self.model
+            self.model = None
+            self._current_model_size = None
+
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+
+            logger.info("Qwen CustomVoice unloaded")
+
+    async def create_voice_prompt(
+        self,
+        audio_path: str,
+        reference_text: str,
+        use_cache: bool = True,
+    ) -> tuple[dict, bool]:
+        """
+        Create voice prompt for CustomVoice.
+
+        CustomVoice doesn't use reference audio — it uses preset speakers.
+        When called for a cloned profile (fallback), uses the default speaker.
+        For preset profiles, the voice_prompt dict is built by the profile
+        service and bypasses this method entirely.
+        """
+        return {
+            "voice_type": "preset",
+            "preset_engine": "qwen_custom_voice",
+            "preset_voice_id": QWEN_CV_DEFAULT_SPEAKER,
+        }, False
+
+    async def combine_voice_prompts(
+        self,
+        audio_paths: list[str],
+        reference_texts: list[str],
+    ) -> tuple[np.ndarray, str]:
+        return await _combine_voice_prompts(audio_paths, reference_texts)
+
+    async def generate(
+        self,
+        text: str,
+        voice_prompt: dict,
+        language: str = "en",
+        seed: Optional[int] = None,
+        instruct: Optional[str] = None,
+    ) -> tuple[np.ndarray, int]:
+        """
+        Generate audio using Qwen CustomVoice.
+
+        Args:
+            text: Text to synthesize
+            voice_prompt: Dict with preset_voice_id (speaker name)
+            language: Language code (zh, en, ja, ko, etc.)
+            seed: Random seed for reproducibility
+            instruct: Natural language instruction for style control
+                      (e.g. "Speak in an angry tone", "Very happy")
+
+        Returns:
+            Tuple of (audio_array, sample_rate)
+        """
+        await self.load_model_async(None)
+
+        speaker = voice_prompt.get("preset_voice_id") or QWEN_CV_DEFAULT_SPEAKER
+
+        def _generate_sync():
+            if seed is not None:
+                torch.manual_seed(seed)
+                if torch.cuda.is_available():
+                    torch.cuda.manual_seed(seed)
+
+            lang_name = LANGUAGE_CODE_TO_NAME.get(language, "auto")
+
+            kwargs = {
+                "text": text,
+                "language": lang_name.capitalize() if lang_name != "auto" else "Auto",
+                "speaker": speaker,
+            }
+
+            # Only pass instruct if non-empty
+            if instruct:
+                kwargs["instruct"] = instruct
+
+            # Inference runs with the process's default HF_HUB_OFFLINE
+            # state. Forcing offline here (issue #462) regressed online
+            # users whose libraries issue legitimate metadata lookups
+            # during generation.
+            wavs, sample_rate = self.model.generate_custom_voice(**kwargs)
+            return wavs[0], sample_rate
+
+        audio, sample_rate = await asyncio.to_thread(_generate_sync)
+        return audio, sample_rate

backend/build_binary.py

@@ -0,0 +1,458 @@
+"""
+PyInstaller build script for creating standalone Python server binary.
+
+Usage:
+    python build_binary.py           # Build default (CPU) server binary
+    python build_binary.py --cuda    # Build CUDA-enabled server binary
+"""
+
+import PyInstaller.__main__
+import argparse
+import logging
+import os
+import platform
+import sys
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+
+def is_apple_silicon():
+    """Check if running on Apple Silicon."""
+    return platform.system() == "Darwin" and platform.machine() == "arm64"
+
+
+def build_server(cuda=False):
+    """Build Python server as standalone binary.
+
+    Args:
+        cuda: If True, build with CUDA support and name the binary
+              voicebox-server-cuda instead of voicebox-server.
+    """
+    backend_dir = Path(__file__).parent
+
+    binary_name = "voicebox-server-cuda" if cuda else "voicebox-server"
+
+    # PyInstaller arguments
+    # CUDA builds use --onedir so we can split the output into two archives:
+    #   1. Server core (~200-400MB) — versioned with the app
+    #   2. CUDA libs (~2GB) — versioned independently (only redownloaded on
+    #      CUDA toolkit / torch major version changes)
+    # CPU builds remain --onefile for simplicity.
+    pack_mode = "--onedir" if cuda else "--onefile"
+    args = [
+        "server.py",  # Use server.py as entry point instead of main.py
+        pack_mode,
+        "--name",
+        binary_name,
+    ]
+
+    # Hide console window on Windows only. On macOS/Linux the sidecar needs
+    # stdout/stderr for Tauri to capture logs.
+    if platform.system() == "Windows":
+        args.append("--noconsole")
+
+    # numpy 2.x / torch ABI mismatch fix: install memmove fallback for
+    # torch.from_numpy() before the app starts. Runtime hooks run after
+    # FrozenImporter is registered so frozen torch/numpy are importable.
+    # Paths are passed relative to backend_dir because os.chdir(backend_dir)
+    # runs before PyInstaller. Absolute paths would get baked into the
+    # generated .spec, breaking reproducible builds on other machines / CI.
+    args.extend(
+        [
+            "--runtime-hook",
+            "pyi_rth_numpy_compat.py",
+            # Stub torch.compiler.disable before transformers imports
+            # flex_attention, which otherwise triggers torch._dynamo →
+            # torch._numpy._ufuncs and crashes at module load under
+            # PyInstaller. See pyi_rth_torch_compiler_disable.py.
+            "--runtime-hook",
+            "pyi_rth_torch_compiler_disable.py",
+            # Per-module collection overrides (e.g. forcing scipy.stats._distn_infrastructure
+            # to bundle .py source alongside .pyc so the runtime hook can source-patch it).
+            "--additional-hooks-dir",
+            "pyi_hooks",
+        ]
+    )
+
+    # Add local qwen_tts path if specified (for editable installs)
+    qwen_tts_path = os.getenv("QWEN_TTS_PATH")
+    if qwen_tts_path and Path(qwen_tts_path).exists():
+        args.extend(["--paths", str(qwen_tts_path)])
+        logger.info("Using local qwen_tts source from: %s", qwen_tts_path)
+
+    # Add common hidden imports
+    args.extend(
+        [
+            "--hidden-import",
+            "backend",
+            "--hidden-import",
+            "backend.main",
+            "--hidden-import",
+            "backend.config",
+            "--hidden-import",
+            "backend.database",
+            "--hidden-import",
+            "backend.models",
+            "--hidden-import",
+            "backend.services.profiles",
+            "--hidden-import",
+            "backend.services.history",
+            "--hidden-import",
+            "backend.services.tts",
+            "--hidden-import",
+            "backend.services.transcribe",
+            "--hidden-import",
+            "backend.utils.platform_detect",
+            "--hidden-import",
+            "backend.backends",
+            "--hidden-import",
+            "backend.backends.pytorch_backend",
+            "--hidden-import",
+            "backend.backends.qwen_custom_voice_backend",
+            "--hidden-import",
+            "backend.utils.audio",
+            "--hidden-import",
+            "backend.utils.cache",
+            "--hidden-import",
+            "backend.utils.progress",
+            "--hidden-import",
+            "backend.utils.hf_progress",
+            "--hidden-import",
+            "backend.services.cuda",
+            "--hidden-import",
+            "backend.services.effects",
+            "--hidden-import",
+            "backend.utils.effects",
+            "--hidden-import",
+            "backend.services.versions",
+            "--hidden-import",
+            "pedalboard",
+            "--hidden-import",
+            "chatterbox",
+            "--hidden-import",
+            "chatterbox.tts_turbo",
+            "--hidden-import",
+            "chatterbox.mtl_tts",
+            "--hidden-import",
+            "backend.backends.chatterbox_backend",
+            "--hidden-import",
+            "backend.backends.chatterbox_turbo_backend",
+            # chatterbox multilingual uses spacy_pkuseg for Chinese word
+            # segmentation, which ships pickled dict files (dicts/default.pkl)
+            # and native .so extensions that --hidden-import alone won't bundle.
+            "--collect-all",
+            "spacy_pkuseg",
+            "--hidden-import",
+            "backend.backends.luxtts_backend",
+            "--hidden-import",
+            "zipvoice",
+            "--hidden-import",
+            "zipvoice.luxvoice",
+            "--collect-all",
+            "zipvoice",
+            "--collect-all",
+            "linacodec",
+            "--hidden-import",
+            "torch",
+            "--hidden-import",
+            "transformers",
+            "--hidden-import",
+            "fastapi",
+            "--hidden-import",
+            "uvicorn",
+            "--hidden-import",
+            "sqlalchemy",
+            # librosa uses lazy_loader which generates .pyi stub files at
+            # install time and reads them at runtime to discover submodules.
+            # --hidden-import alone doesn't bundle the stubs, causing
+            # "Cannot load imports from non-existent stub" at runtime.
+            "--collect-all",
+            "lazy_loader",
+            "--collect-all",
+            "librosa",
+            "--hidden-import",
+            "soundfile",
+            "--hidden-import",
+            "qwen_tts",
+            "--hidden-import",
+            "qwen_tts.inference",
+            "--hidden-import",
+            "qwen_tts.inference.qwen3_tts_model",
+            "--hidden-import",
+            "qwen_tts.inference.qwen3_tts_tokenizer",
+            "--hidden-import",
+            "qwen_tts.core",
+            "--hidden-import",
+            "qwen_tts.cli",
+            "--copy-metadata",
+            "qwen-tts",
+            "--copy-metadata",
+            "requests",
+            "--copy-metadata",
+            "transformers",
+            "--copy-metadata",
+            "huggingface-hub",
+            "--copy-metadata",
+            "tokenizers",
+            "--copy-metadata",
+            "safetensors",
+            "--copy-metadata",
+            "tqdm",
+            "--hidden-import",
+            "requests",
+            # qwen_tts uses inspect.getsource() at runtime to locate
+            # modeling_qwen3_tts.py — needs physical .py source files bundled
+            "--collect-all",
+            "qwen_tts",
+            # Fix for pkg_resources and jaraco namespace packages
+            "--hidden-import",
+            "pkg_resources.extern",
+            "--collect-submodules",
+            "jaraco",
+            # inflect uses typeguard @typechecked which calls inspect.getsource()
+            # at import time — needs .py source files, not just .pyc bytecode
+            "--collect-all",
+            "inflect",
+            # perth ships pretrained watermark model files (hparams.yaml, .pth.tar)
+            # in perth/perth_net/pretrained/ — needed by chatterbox at runtime
+            "--collect-all",
+            "perth",
+            # piper_phonemize ships espeak-ng-data/ (phoneme tables, language dicts)
+            # needed by LuxTTS for text-to-phoneme conversion
+            "--collect-all",
+            "piper_phonemize",
+            # HumeAI TADA — speech-language model using Llama + flow matching
+            "--hidden-import",
+            "backend.backends.hume_backend",
+            "--hidden-import",
+            "tada",
+            "--hidden-import",
+            "tada.modules",
+            "--hidden-import",
+            "tada.modules.tada",
+            "--hidden-import",
+            "tada.modules.encoder",
+            "--hidden-import",
+            "tada.modules.decoder",
+            "--hidden-import",
+            "tada.modules.aligner",
+            "--hidden-import",
+            "tada.modules.acoustic_spkr_verf",
+            "--hidden-import",
+            "tada.nn",
+            "--hidden-import",
+            "tada.nn.vibevoice",
+            "--hidden-import",
+            "tada.utils",
+            "--hidden-import",
+            "tada.utils.gray_code",
+            "--hidden-import",
+            "tada.utils.text",
+            # DAC shim — provides dac.nn.layers.Snake1d without the real
+            # descript-audio-codec package (which pulls onnx/tensorboard via
+            # descript-audiotools). The shim is in backend/utils/dac_shim.py.
+            "--hidden-import",
+            "backend.utils.dac_shim",
+            "--hidden-import",
+            "torchaudio",
+            "--collect-submodules",
+            "tada",
+            # Kokoro 82M — lightweight TTS engine using misaki G2P
+            # collect-all is required because transformers introspects .py source
+            # files at runtime (e.g. _can_set_attn_implementation opens the class
+            # file); hidden-import alone only bundles bytecode.
+            "--hidden-import",
+            "backend.backends.kokoro_backend",
+            "--collect-all",
+            "kokoro",
+            # misaki ships G2P data files (dictionaries, phoneme tables)
+            # that must be bundled for espeak/en/ja/zh G2P to work
+            "--collect-all",
+            "misaki",
+            # language_tags ships JSON data files (index.json etc.) loaded at
+            # runtime via: misaki → phonemizer → segments → csvw → language_tags
+            "--collect-all",
+            "language_tags",
+            # espeakng_loader ships the entire espeak-ng-data directory (369 files)
+            # loaded at import time by misaki.espeak via get_data_path()
+            "--collect-all",
+            "espeakng_loader",
+            # spacy en_core_web_sm model — misaki.en tries to spacy.cli.download()
+            # at runtime if not found, which calls pip as a subprocess and crashes
+            # the frozen binary. Bundle the model so spacy.util.is_package() passes.
+            "--collect-all",
+            "en_core_web_sm",
+            "--copy-metadata",
+            "en_core_web_sm",
+            "--hidden-import",
+            "en_core_web_sm",
+            # unidic-lite ships the MeCab dictionary used by fugashi (pulled in
+            # by misaki[ja]). The dict lives in unidic_lite/dicdir/ and is
+            # discovered via the package's DICDIR constant, so the data files
+            # must be collected or Japanese Kokoro voices crash at runtime.
+            "--collect-all",
+            "unidic_lite",
+            "--hidden-import",
+            "loguru",
+        ]
+    )
+
+    # Add CUDA-specific hidden imports
+    if cuda:
+        logger.info("Building with CUDA support")
+        args.extend(
+            [
+                "--hidden-import",
+                "torch.cuda",
+                "--hidden-import",
+                "torch.backends.cudnn",
+            ]
+        )
+    else:
+        # Exclude NVIDIA CUDA packages from CPU-only builds to keep binary small.
+        # When building from a venv with CUDA torch installed, PyInstaller would
+        # bundle ~3GB of NVIDIA shared libraries. We exclude both the Python
+        # modules and the binary DLLs.
+        nvidia_packages = [
+            "nvidia",
+            "nvidia.cublas",
+            "nvidia.cuda_cupti",
+            "nvidia.cuda_nvrtc",
+            "nvidia.cuda_runtime",
+            "nvidia.cudnn",
+            "nvidia.cufft",
+            "nvidia.curand",
+            "nvidia.cusolver",
+            "nvidia.cusparse",
+            "nvidia.nccl",
+            "nvidia.nvjitlink",
+            "nvidia.nvtx",
+        ]
+        for pkg in nvidia_packages:
+            args.extend(["--exclude-module", pkg])
+
+    # Add MLX-specific imports if building on Apple Silicon (never for CUDA builds)
+    if is_apple_silicon() and not cuda:
+        logger.info("Building for Apple Silicon - including MLX dependencies")
+        args.extend(
+            [
+                "--hidden-import",
+                "backend.backends.mlx_backend",
+                "--hidden-import",
+                "mlx",
+                "--hidden-import",
+                "mlx.core",
+                "--hidden-import",
+                "mlx.nn",
+                "--hidden-import",
+                "mlx_audio",
+                "--hidden-import",
+                "mlx_audio.tts",
+                "--hidden-import",
+                "mlx_audio.stt",
+                "--collect-submodules",
+                "mlx",
+                "--collect-submodules",
+                "mlx_audio",
+                # Use --collect-all so PyInstaller bundles both data files AND
+                # native shared libraries (.dylib, .metallib) for MLX.
+                # Previously only --collect-data was used, which caused MLX to
+                # raise OSError at runtime inside the bundled binary because
+                # the Metal shader libraries were missing.
+                "--collect-all",
+                "mlx",
+                "--collect-all",
+                "mlx_audio",
+            ]
+        )
+    elif not cuda:
+        logger.info("Building for non-Apple Silicon platform - PyTorch only")
+
+    dist_dir = str(backend_dir / "dist")
+    build_dir = str(backend_dir / "build")
+
+    args.extend(
+        [
+            "--distpath",
+            dist_dir,
+            "--workpath",
+            build_dir,
+            "--noconfirm",
+            "--clean",
+        ]
+    )
+
+    # Change to backend directory
+    os.chdir(backend_dir)
+
+    # For CPU builds on Windows, ensure we're using CPU-only torch.
+    # If CUDA torch is installed (local dev), swap to CPU torch before building,
+    # then restore CUDA torch after. This prevents PyInstaller from bundling
+    # ~3GB of CUDA DLLs into the CPU binary.
+    restore_cuda = False
+    if not cuda and platform.system() == "Windows":
+        import subprocess
+
+        result = subprocess.run(
+            [sys.executable, "-c", "import torch; print(torch.version.cuda or '')"], capture_output=True, text=True
+        )
+        has_cuda_torch = bool(result.stdout.strip())
+        if has_cuda_torch:
+            logger.info("CUDA torch detected — installing CPU torch for CPU build...")
+            subprocess.run(
+                [
+                    sys.executable,
+                    "-m",
+                    "pip",
+                    "install",
+                    "torch",
+                    "torchvision",
+                    "torchaudio",
+                    "--index-url",
+                    "https://download.pytorch.org/whl/cpu",
+                    "--force-reinstall",
+                    "-q",
+                ],
+                check=True,
+            )
+            restore_cuda = True
+
+    # Run PyInstaller
+    try:
+        PyInstaller.__main__.run(args)
+    finally:
+        # Restore CUDA torch if we swapped it out (even on build failure)
+        if restore_cuda:
+            logger.info("Restoring CUDA torch...")
+            import subprocess
+
+            subprocess.run(
+                [
+                    sys.executable,
+                    "-m",
+                    "pip",
+                    "install",
+                    "torch",
+                    "torchvision",
+                    "torchaudio",
+                    "--index-url",
+                    "https://download.pytorch.org/whl/cu128",
+                    "--force-reinstall",
+                    "-q",
+                ],
+                check=True,
+            )
+
+    logger.info("Binary built in %s", backend_dir / "dist" / binary_name)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Build voicebox-server binary")
+    parser.add_argument(
+        "--cuda",
+        action="store_true",
+        help="Build CUDA-enabled binary (voicebox-server-cuda)",
+    )
+    cli_args = parser.parse_args()
+    build_server(cuda=cli_args.cuda)

backend/config.py

@@ -0,0 +1,133 @@
+"""
+Configuration module for voicebox backend.
+
+Handles data directory configuration for production bundling.
+"""
+
+import logging
+import os
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+# Allow users to override the HuggingFace model download directory.
+# Set VOICEBOX_MODELS_DIR to an absolute path before starting the server.
+# This sets HF_HUB_CACHE so all huggingface_hub downloads go to that path.
+_custom_models_dir = os.environ.get("VOICEBOX_MODELS_DIR")
+if _custom_models_dir:
+    os.environ["HF_HUB_CACHE"] = _custom_models_dir
+    logger.info("Model download path set to: %s", _custom_models_dir)
+
+# Default data directory (used in development)
+_data_dir = Path("data").resolve()
+
+
+def _path_relative_to_any_data_dir(path: Path) -> Path | None:
+    """Extract the path within a data dir from an absolute or relative path."""
+    parts = path.parts
+    for idx, part in enumerate(parts):
+        if part != "data":
+            continue
+
+        tail = parts[idx + 1 :]
+        if tail:
+            return Path(*tail)
+        return Path()
+
+    return None
+
+
+def set_data_dir(path: str | Path):
+    """
+    Set the data directory path.
+
+    Args:
+        path: Path to the data directory
+    """
+    global _data_dir
+    _data_dir = Path(path).resolve()
+    _data_dir.mkdir(parents=True, exist_ok=True)
+    logger.info("Data directory set to: %s", _data_dir)
+
+
+def get_data_dir() -> Path:
+    """
+    Get the data directory path.
+
+    Returns:
+        Path to the data directory
+    """
+    return _data_dir
+
+
+def to_storage_path(path: str | Path) -> str:
+    """Convert a filesystem path to a DB-safe path relative to the data dir."""
+    resolved_path = Path(path).resolve()
+
+    relative_to_any_data_dir = _path_relative_to_any_data_dir(resolved_path)
+    if relative_to_any_data_dir is not None:
+        return str(relative_to_any_data_dir)
+
+    try:
+        return str(resolved_path.relative_to(_data_dir))
+    except ValueError:
+        return str(resolved_path)
+
+
+def resolve_storage_path(path: str | Path | None) -> Path | None:
+    """Resolve a DB-stored path against the configured data dir."""
+    if path is None:
+        return None
+
+    stored_path = Path(path)
+    if stored_path.is_absolute():
+        rebased_path = _path_relative_to_any_data_dir(stored_path)
+        if rebased_path is not None:
+            candidate = (_data_dir / rebased_path).resolve()
+            if candidate.exists() or not stored_path.exists():
+                return candidate
+
+        return stored_path
+
+    # 0.3.0 records sometimes stored relative paths with the data-dir name
+    # baked in (e.g. "data/profiles/..."). Joining those directly with
+    # _data_dir produces a spurious "<data_dir>/data/profiles/..." nest.
+    if stored_path.parts and stored_path.parts[0] == "data":
+        stored_path = (
+            Path(*stored_path.parts[1:]) if len(stored_path.parts) > 1 else Path()
+        )
+
+    return (_data_dir / stored_path).resolve()
+
+
+def get_db_path() -> Path:
+    """Get database file path."""
+    return _data_dir / "voicebox.db"
+
+
+def get_profiles_dir() -> Path:
+    """Get profiles directory path."""
+    path = _data_dir / "profiles"
+    path.mkdir(parents=True, exist_ok=True)
+    return path
+
+
+def get_generations_dir() -> Path:
+    """Get generations directory path."""
+    path = _data_dir / "generations"
+    path.mkdir(parents=True, exist_ok=True)
+    return path
+
+
+def get_cache_dir() -> Path:
+    """Get cache directory path."""
+    path = _data_dir / "cache"
+    path.mkdir(parents=True, exist_ok=True)
+    return path
+
+
+def get_models_dir() -> Path:
+    """Get models directory path."""
+    path = _data_dir / "models"
+    path.mkdir(parents=True, exist_ok=True)
+    return path

backend/database/__init__.py

@@ -0,0 +1,44 @@
+"""Database package — ORM models, session management, and migrations.
+
+Re-exports all public symbols so that ``from .database import get_db``
+and ``from .database import Generation as DBGeneration`` continue to work
+without changing any importers.
+"""
+
+from .models import (
+    Base,
+    AudioChannel,
+    ChannelDeviceMapping,
+    EffectPreset,
+    Generation,
+    GenerationVersion,
+    ProfileChannelMapping,
+    ProfileSample,
+    Project,
+    Story,
+    StoryItem,
+    VoiceProfile,
+)
+from .session import engine, SessionLocal, _db_path, init_db, get_db
+
+__all__ = [
+    # Models
+    "Base",
+    "AudioChannel",
+    "ChannelDeviceMapping",
+    "EffectPreset",
+    "Generation",
+    "GenerationVersion",
+    "ProfileChannelMapping",
+    "ProfileSample",
+    "Project",
+    "Story",
+    "StoryItem",
+    "VoiceProfile",
+    # Session
+    "engine",
+    "SessionLocal",
+    "_db_path",
+    "init_db",
+    "get_db",
+]

backend/database/migrations.py

@@ -0,0 +1,226 @@
+"""Column-level migrations for the voicebox SQLite database.
+
+Why not Alembic?  voicebox is a single-user desktop app shipping as a
+PyInstaller binary.  Every user has exactly one SQLite file.  Alembic's
+strengths -- migration tracking across environments, rollback, team
+coordination -- don't apply here and would add bundling complexity
+(alembic.ini, env.py, versions/ directory all need to survive
+PyInstaller).  The column-existence checks below are idempotent, run in
+<50 ms on startup, and have worked reliably across 12 schema changes.
+If the project ever moves to a server-based deployment or Postgres, this
+decision should be revisited.
+
+Adding a new migration:
+    1. Append a new ``_migrate_*`` helper at the bottom of this file.
+    2. Call it from ``run_migrations()`` in the appropriate spot.
+    3. The helper should check column/table existence before acting
+       (idempotent) and print a short message when it does real work.
+"""
+
+import logging
+
+from sqlalchemy import inspect, text
+
+logger = logging.getLogger(__name__)
+
+
+def run_migrations(engine) -> None:
+    """Run all schema migrations.  Safe to call on every startup."""
+    inspector = inspect(engine)
+    tables = set(inspector.get_table_names())
+
+    _migrate_story_items(engine, inspector, tables)
+    _migrate_profiles(engine, inspector, tables)
+    _migrate_generations(engine, inspector, tables)
+    _migrate_effect_presets(engine, inspector, tables)
+    _migrate_generation_versions(engine, inspector, tables)
+    _normalize_storage_paths(engine, tables)
+
+
+# -- helpers ---------------------------------------------------------------
+
+def _get_columns(inspector, table: str) -> set[str]:
+    return {col["name"] for col in inspector.get_columns(table)}
+
+
+def _add_column(engine, table: str, column_sql: str, label: str) -> None:
+    """Add a column if it doesn't already exist."""
+    with engine.connect() as conn:
+        conn.execute(text(f"ALTER TABLE {table} ADD COLUMN {column_sql}"))
+        conn.commit()
+    logger.info("Added %s column to %s", label, table)
+
+
+# -- per-table migrations --------------------------------------------------
+
+def _migrate_story_items(engine, inspector, tables: set[str]) -> None:
+    if "story_items" not in tables:
+        return
+
+    columns = _get_columns(inspector, "story_items")
+
+    # Replace position-based ordering with absolute timecodes
+    if "position" in columns:
+        logger.info("Migrating story_items: removing position column, using start_time_ms")
+        with engine.connect() as conn:
+            if "start_time_ms" not in columns:
+                conn.execute(text(
+                    "ALTER TABLE story_items ADD COLUMN start_time_ms INTEGER DEFAULT 0"
+                ))
+                result = conn.execute(text("""
+                    SELECT si.id, si.story_id, si.position, g.duration
+                    FROM story_items si
+                    JOIN generations g ON si.generation_id = g.id
+                    ORDER BY si.story_id, si.position
+                """))
+                current_story_id = None
+                current_time_ms = 0
+                for item_id, story_id, _position, duration in result.fetchall():
+                    if story_id != current_story_id:
+                        current_story_id = story_id
+                        current_time_ms = 0
+                    conn.execute(
+                        text("UPDATE story_items SET start_time_ms = :time WHERE id = :id"),
+                        {"time": current_time_ms, "id": item_id},
+                    )
+                    current_time_ms += int((duration or 0) * 1000) + 200
+                conn.commit()
+
+            # Recreate table without the position column (SQLite lacks DROP COLUMN)
+            conn.execute(text("""
+                CREATE TABLE story_items_new (
+                    id VARCHAR PRIMARY KEY,
+                    story_id VARCHAR NOT NULL,
+                    generation_id VARCHAR NOT NULL,
+                    start_time_ms INTEGER NOT NULL DEFAULT 0,
+                    track INTEGER NOT NULL DEFAULT 0,
+                    trim_start_ms INTEGER NOT NULL DEFAULT 0,
+                    trim_end_ms INTEGER NOT NULL DEFAULT 0,
+                    version_id VARCHAR,
+                    created_at DATETIME,
+                    FOREIGN KEY (story_id) REFERENCES stories(id),
+                    FOREIGN KEY (generation_id) REFERENCES generations(id)
+                )
+            """))
+            conn.execute(text("""
+                INSERT INTO story_items_new (id, story_id, generation_id, start_time_ms, track, trim_start_ms, trim_end_ms, version_id, created_at)
+                SELECT id, story_id, generation_id, start_time_ms,
+                    COALESCE(track, 0), COALESCE(trim_start_ms, 0), COALESCE(trim_end_ms, 0), version_id, created_at
+                FROM story_items
+            """))
+            conn.execute(text("DROP TABLE story_items"))
+            conn.execute(text("ALTER TABLE story_items_new RENAME TO story_items"))
+            conn.commit()
+
+        # Re-read after table recreation
+        columns = _get_columns(inspector, "story_items")
+
+    if "track" not in columns:
+        _add_column(engine, "story_items", "track INTEGER NOT NULL DEFAULT 0", "track")
+    # Re-read so subsequent checks see new columns
+    columns = _get_columns(inspector, "story_items")
+    if "trim_start_ms" not in columns:
+        _add_column(engine, "story_items", "trim_start_ms INTEGER NOT NULL DEFAULT 0", "trim_start_ms")
+    if "trim_end_ms" not in columns:
+        _add_column(engine, "story_items", "trim_end_ms INTEGER NOT NULL DEFAULT 0", "trim_end_ms")
+    if "version_id" not in columns:
+        _add_column(engine, "story_items", "version_id VARCHAR", "version_id")
+
+
+def _migrate_profiles(engine, inspector, tables: set[str]) -> None:
+    if "profiles" not in tables:
+        return
+    columns = _get_columns(inspector, "profiles")
+    if "avatar_path" not in columns:
+        _add_column(engine, "profiles", "avatar_path VARCHAR", "avatar_path")
+    if "effects_chain" not in columns:
+        _add_column(engine, "profiles", "effects_chain TEXT", "effects_chain")
+    # Voice type system — v0.3.x
+    if "voice_type" not in columns:
+        _add_column(engine, "profiles", "voice_type VARCHAR DEFAULT 'cloned'", "voice_type")
+    if "preset_engine" not in columns:
+        _add_column(engine, "profiles", "preset_engine VARCHAR", "preset_engine")
+    if "preset_voice_id" not in columns:
+        _add_column(engine, "profiles", "preset_voice_id VARCHAR", "preset_voice_id")
+    if "design_prompt" not in columns:
+        _add_column(engine, "profiles", "design_prompt TEXT", "design_prompt")
+    if "default_engine" not in columns:
+        _add_column(engine, "profiles", "default_engine VARCHAR", "default_engine")
+
+
+def _migrate_generations(engine, inspector, tables: set[str]) -> None:
+    if "generations" not in tables:
+        return
+    columns = _get_columns(inspector, "generations")
+    if "status" not in columns:
+        _add_column(engine, "generations", "status VARCHAR DEFAULT 'completed'", "status")
+    if "error" not in columns:
+        _add_column(engine, "generations", "error TEXT", "error")
+    if "engine" not in columns:
+        _add_column(engine, "generations", "engine VARCHAR DEFAULT 'qwen'", "engine")
+    # Re-read after engine column (variable name shadows outer scope in old code)
+    columns = _get_columns(inspector, "generations")
+    if "model_size" not in columns:
+        _add_column(engine, "generations", "model_size VARCHAR", "model_size")
+    if "is_favorited" not in columns:
+        _add_column(engine, "generations", "is_favorited BOOLEAN DEFAULT 0", "is_favorited")
+
+
+def _migrate_effect_presets(engine, inspector, tables: set[str]) -> None:
+    if "effect_presets" not in tables:
+        return
+    columns = _get_columns(inspector, "effect_presets")
+    if "sort_order" not in columns:
+        _add_column(engine, "effect_presets", "sort_order INTEGER DEFAULT 100", "sort_order")
+
+
+def _migrate_generation_versions(engine, inspector, tables: set[str]) -> None:
+    if "generation_versions" not in tables:
+        return
+    columns = _get_columns(inspector, "generation_versions")
+    if "source_version_id" not in columns:
+        _add_column(engine, "generation_versions", "source_version_id VARCHAR", "source_version_id")
+
+
+def _normalize_storage_paths(engine, tables: set[str]) -> None:
+    """Normalize stored file paths to be relative to the configured data dir."""
+    from pathlib import Path
+
+    from ..config import get_data_dir, to_storage_path, resolve_storage_path
+
+    data_dir = get_data_dir()
+
+    path_columns = [
+        ("generations", "audio_path"),
+        ("generation_versions", "audio_path"),
+        ("profile_samples", "audio_path"),
+        ("profiles", "avatar_path"),
+    ]
+
+    total_fixed = 0
+    with engine.connect() as conn:
+        for table, column in path_columns:
+            if table not in tables:
+                continue
+            rows = conn.execute(
+                text(f"SELECT id, {column} FROM {table} WHERE {column} IS NOT NULL")
+            ).fetchall()
+            for row_id, path_val in rows:
+                if not path_val:
+                    continue
+                p = Path(path_val)
+                resolved = resolve_storage_path(p)
+                if resolved is None:
+                    continue
+
+                normalized = to_storage_path(resolved)
+
+                if normalized != path_val:
+                    conn.execute(
+                        text(f"UPDATE {table} SET {column} = :path WHERE id = :id"),
+                        {"path": normalized, "id": row_id},
+                    )
+                    total_fixed += 1
+        if total_fixed > 0:
+            conn.commit()
+            logger.info("Normalized %d stored file paths", total_fixed)

backend/database/models.py

@@ -0,0 +1,169 @@
+"""ORM model definitions for the voicebox SQLite database."""
+
+from datetime import datetime
+import uuid
+
+from sqlalchemy import Column, String, Integer, Float, DateTime, Text, ForeignKey, Boolean
+from sqlalchemy.ext.declarative import declarative_base
+
+Base = declarative_base()
+
+
+class VoiceProfile(Base):
+    """Voice profile.
+
+    voice_type discriminates three flavours:
+      - "cloned"   — traditional reference-audio profiles (all cloning engines)
+      - "preset"   — engine-specific pre-built voice (e.g. Kokoro voices)
+      - "designed"  — text-described voice (e.g. Qwen CustomVoice, future)
+    """
+
+    __tablename__ = "profiles"
+
+    id = Column(String, primary_key=True, default=lambda: str(uuid.uuid4()))
+    name = Column(String, unique=True, nullable=False)
+    description = Column(Text)
+    language = Column(String, default="en")
+    avatar_path = Column(String, nullable=True)
+    effects_chain = Column(Text, nullable=True)
+
+    # Voice type system — added v0.3.x
+    voice_type = Column(String, default="cloned")  # "cloned" | "preset" | "designed"
+    preset_engine = Column(String, nullable=True)   # e.g. "kokoro" — only for preset
+    preset_voice_id = Column(String, nullable=True)  # e.g. "am_adam" — only for preset
+    design_prompt = Column(Text, nullable=True)      # text description — only for designed
+    default_engine = Column(String, nullable=True)   # auto-selected engine, locked for preset
+
+    created_at = Column(DateTime, default=datetime.utcnow)
+    updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
+
+
+class ProfileSample(Base):
+    """Audio sample attached to a voice profile."""
+
+    __tablename__ = "profile_samples"
+
+    id = Column(String, primary_key=True, default=lambda: str(uuid.uuid4()))
+    profile_id = Column(String, ForeignKey("profiles.id"), nullable=False)
+    audio_path = Column(String, nullable=False)
+    reference_text = Column(Text, nullable=False)
+
+
+class Generation(Base):
+    """A single TTS generation."""
+
+    __tablename__ = "generations"
+
+    id = Column(String, primary_key=True, default=lambda: str(uuid.uuid4()))
+    profile_id = Column(String, ForeignKey("profiles.id"), nullable=False)
+    text = Column(Text, nullable=False)
+    language = Column(String, default="en")
+    audio_path = Column(String, nullable=True)
+    duration = Column(Float, nullable=True)
+    seed = Column(Integer)
+    instruct = Column(Text)
+    engine = Column(String, default="qwen")
+    model_size = Column(String, nullable=True)
+    status = Column(String, default="completed")
+    error = Column(Text, nullable=True)
+    is_favorited = Column(Boolean, default=False)
+    created_at = Column(DateTime, default=datetime.utcnow)
+
+
+class Story(Base):
+    """A story that sequences multiple generations."""
+
+    __tablename__ = "stories"
+
+    id = Column(String, primary_key=True, default=lambda: str(uuid.uuid4()))
+    name = Column(String, nullable=False)
+    description = Column(Text)
+    created_at = Column(DateTime, default=datetime.utcnow)
+    updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
+
+
+class StoryItem(Base):
+    """Links a generation to a story at a specific timecode."""
+
+    __tablename__ = "story_items"
+
+    id = Column(String, primary_key=True, default=lambda: str(uuid.uuid4()))
+    story_id = Column(String, ForeignKey("stories.id"), nullable=False)
+    generation_id = Column(String, ForeignKey("generations.id"), nullable=False)
+    version_id = Column(String, ForeignKey("generation_versions.id"), nullable=True)
+    start_time_ms = Column(Integer, nullable=False, default=0)
+    track = Column(Integer, nullable=False, default=0)
+    trim_start_ms = Column(Integer, nullable=False, default=0)
+    trim_end_ms = Column(Integer, nullable=False, default=0)
+    created_at = Column(DateTime, default=datetime.utcnow)
+
+
+class Project(Base):
+    """Audio studio project (JSON blob)."""
+
+    __tablename__ = "projects"
+
+    id = Column(String, primary_key=True, default=lambda: str(uuid.uuid4()))
+    name = Column(String, nullable=False)
+    data = Column(Text)
+    created_at = Column(DateTime, default=datetime.utcnow)
+    updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
+
+
+class GenerationVersion(Base):
+    """A version of a generation's audio (original, processed, alternate takes)."""
+
+    __tablename__ = "generation_versions"
+
+    id = Column(String, primary_key=True, default=lambda: str(uuid.uuid4()))
+    generation_id = Column(String, ForeignKey("generations.id"), nullable=False)
+    label = Column(String, nullable=False)
+    audio_path = Column(String, nullable=False)
+    effects_chain = Column(Text, nullable=True)
+    source_version_id = Column(String, ForeignKey("generation_versions.id"), nullable=True)
+    is_default = Column(Boolean, default=False)
+    created_at = Column(DateTime, default=datetime.utcnow)
+
+
+class EffectPreset(Base):
+    """Saved effect chain preset."""
+
+    __tablename__ = "effect_presets"
+
+    id = Column(String, primary_key=True, default=lambda: str(uuid.uuid4()))
+    name = Column(String, unique=True, nullable=False)
+    description = Column(Text, nullable=True)
+    effects_chain = Column(Text, nullable=False)
+    is_builtin = Column(Boolean, default=False)
+    sort_order = Column(Integer, default=100)
+    created_at = Column(DateTime, default=datetime.utcnow)
+
+
+class AudioChannel(Base):
+    """Audio output channel (bus)."""
+
+    __tablename__ = "audio_channels"
+
+    id = Column(String, primary_key=True, default=lambda: str(uuid.uuid4()))
+    name = Column(String, nullable=False)
+    is_default = Column(Boolean, default=False)
+    created_at = Column(DateTime, default=datetime.utcnow)
+
+
+class ChannelDeviceMapping(Base):
+    """Mapping between a channel and an OS audio device."""
+
+    __tablename__ = "channel_device_mappings"
+
+    id = Column(String, primary_key=True, default=lambda: str(uuid.uuid4()))
+    channel_id = Column(String, ForeignKey("audio_channels.id"), nullable=False)
+    device_id = Column(String, nullable=False)
+
+
+class ProfileChannelMapping(Base):
+    """Many-to-many mapping between voice profiles and audio channels."""
+
+    __tablename__ = "profile_channel_mappings"
+
+    profile_id = Column(String, ForeignKey("profiles.id"), primary_key=True)
+    channel_id = Column(String, ForeignKey("audio_channels.id"), primary_key=True)

backend/database/seed.py

@@ -0,0 +1,73 @@
+"""Post-migration data seeding and backfills."""
+
+import json
+import logging
+import uuid
+
+from .. import config
+
+logger = logging.getLogger(__name__)
+
+
+def backfill_generation_versions(SessionLocal, Generation, GenerationVersion) -> None:
+    """Create 'clean' version entries for generations that predate the versions feature."""
+    db = SessionLocal()
+    try:
+        existing_version_gen_ids = {
+            row[0] for row in db.query(GenerationVersion.generation_id).all()
+        }
+        generations = db.query(Generation).filter(
+            Generation.status == "completed",
+            Generation.audio_path.isnot(None),
+            Generation.audio_path != "",
+        ).all()
+
+        count = 0
+        for gen in generations:
+            if gen.id in existing_version_gen_ids:
+                continue
+            resolved_audio_path = config.resolve_storage_path(gen.audio_path)
+            if resolved_audio_path is None or not resolved_audio_path.exists():
+                continue
+            version = GenerationVersion(
+                id=str(uuid.uuid4()),
+                generation_id=gen.id,
+                label="clean",
+                audio_path=gen.audio_path,
+                effects_chain=None,
+                is_default=True,
+            )
+            db.add(version)
+            count += 1
+
+        if count > 0:
+            db.commit()
+            logger.info("Backfilled %d generation version entries", count)
+    finally:
+        db.close()
+
+
+def seed_builtin_presets(SessionLocal, EffectPreset) -> None:
+    """Ensure built-in effect presets exist in the database."""
+    from ..utils.effects import BUILTIN_PRESETS
+
+    db = SessionLocal()
+    try:
+        for idx, (_key, preset_data) in enumerate(BUILTIN_PRESETS.items()):
+            sort_order = preset_data.get("sort_order", idx)
+            existing = db.query(EffectPreset).filter_by(name=preset_data["name"]).first()
+            if not existing:
+                preset = EffectPreset(
+                    id=str(uuid.uuid4()),
+                    name=preset_data["name"],
+                    description=preset_data.get("description"),
+                    effects_chain=json.dumps(preset_data["effects_chain"]),
+                    is_builtin=True,
+                    sort_order=sort_order,
+                )
+                db.add(preset)
+            elif existing.sort_order != sort_order:
+                existing.sort_order = sort_order
+        db.commit()
+    finally:
+        db.close()

backend/database/session.py

@@ -0,0 +1,78 @@
+"""Engine creation, initialization, and session management."""
+
+import logging
+import uuid
+
+from sqlalchemy import create_engine
+from sqlalchemy.orm import sessionmaker
+
+from .. import config
+from .models import (
+    Base,
+    AudioChannel,
+    EffectPreset,
+    Generation,
+    GenerationVersion,
+    ProfileChannelMapping,
+    VoiceProfile,
+)
+from .migrations import run_migrations
+from .seed import backfill_generation_versions, seed_builtin_presets
+
+logger = logging.getLogger(__name__)
+
+# Initialized by init_db()
+engine = None
+SessionLocal = None
+_db_path = None
+
+
+def init_db() -> None:
+    """Initialize the database engine, run migrations, create tables, and seed data."""
+    global engine, SessionLocal, _db_path
+
+    _db_path = config.get_db_path()
+    _db_path.parent.mkdir(parents=True, exist_ok=True)
+
+    engine = create_engine(
+        f"sqlite:///{_db_path}",
+        connect_args={"check_same_thread": False},
+    )
+
+    SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+
+    run_migrations(engine)
+    Base.metadata.create_all(bind=engine)
+
+    # Create default audio channel if it doesn't exist
+    db = SessionLocal()
+    try:
+        default_channel = db.query(AudioChannel).filter(AudioChannel.is_default == True).first()
+        if not default_channel:
+            default_channel = AudioChannel(
+                id=str(uuid.uuid4()),
+                name="Default",
+                is_default=True,
+            )
+            db.add(default_channel)
+
+            for profile in db.query(VoiceProfile).all():
+                db.add(ProfileChannelMapping(
+                    profile_id=profile.id,
+                    channel_id=default_channel.id,
+                ))
+            db.commit()
+    finally:
+        db.close()
+
+    backfill_generation_versions(SessionLocal, Generation, GenerationVersion)
+    seed_builtin_presets(SessionLocal, EffectPreset)
+
+
+def get_db():
+    """Yield a database session (FastAPI dependency)."""
+    db = SessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()

backend/main.py

@@ -0,0 +1,45 @@
+"""Entry point for the voicebox backend.
+
+Imports the configured FastAPI app and provides a ``python -m backend.main``
+entry point for development.
+"""
+
+import argparse
+import uvicorn
+
+from .app import app  # noqa: F401 -- re-export for uvicorn "backend.main:app"
+from . import config, database
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="voicebox backend server")
+    parser.add_argument(
+        "--host",
+        type=str,
+        default="127.0.0.1",
+        help="Host to bind to (use 0.0.0.0 for remote access)",
+    )
+    parser.add_argument(
+        "--port",
+        type=int,
+        default=8000,
+        help="Port to bind to",
+    )
+    parser.add_argument(
+        "--data-dir",
+        type=str,
+        default=None,
+        help="Data directory for database, profiles, and generated audio",
+    )
+    args = parser.parse_args()
+
+    if args.data_dir:
+        config.set_data_dir(args.data_dir)
+
+    database.init_db()
+
+    uvicorn.run(
+        "backend.main:app",
+        host=args.host,
+        port=args.port,
+        reload=False,
+    )

backend/models.py

@@ -0,0 +1,521 @@
+"""
+Pydantic models for request/response validation.
+"""
+
+from pydantic import BaseModel, Field
+from typing import Optional, List
+from datetime import datetime
+
+
+class VoiceProfileCreate(BaseModel):
+    """Request model for creating a voice profile."""
+
+    name: str = Field(..., min_length=1, max_length=100)
+    description: Optional[str] = Field(None, max_length=500)
+    language: str = Field(
+        default="en", pattern="^(zh|en|ja|ko|de|fr|ru|pt|es|it|he|ar|da|el|fi|hi|ms|nl|no|pl|sv|sw|tr)$"
+    )
+    voice_type: Optional[str] = Field(default="cloned", pattern="^(cloned|preset|designed)$")
+    preset_engine: Optional[str] = Field(None, max_length=50)
+    preset_voice_id: Optional[str] = Field(None, max_length=100)
+    design_prompt: Optional[str] = Field(None, max_length=2000)
+    default_engine: Optional[str] = Field(None, max_length=50)
+
+
+class VoiceProfileResponse(BaseModel):
+    """Response model for voice profile."""
+
+    id: str
+    name: str
+    description: Optional[str]
+    language: str
+    avatar_path: Optional[str] = None
+    effects_chain: Optional[List["EffectConfig"]] = None
+    voice_type: str = "cloned"
+    preset_engine: Optional[str] = None
+    preset_voice_id: Optional[str] = None
+    design_prompt: Optional[str] = None
+    default_engine: Optional[str] = None
+    generation_count: int = 0
+    sample_count: int = 0
+    created_at: datetime
+    updated_at: datetime
+
+    class Config:
+        from_attributes = True
+
+
+class ProfileSampleCreate(BaseModel):
+    """Request model for adding a sample to a profile."""
+
+    reference_text: str = Field(..., min_length=1, max_length=1000)
+
+
+class ProfileSampleUpdate(BaseModel):
+    """Request model for updating a profile sample."""
+
+    reference_text: str = Field(..., min_length=1, max_length=1000)
+
+
+class ProfileSampleResponse(BaseModel):
+    """Response model for profile sample."""
+
+    id: str
+    profile_id: str
+    audio_path: str
+    reference_text: str
+
+    class Config:
+        from_attributes = True
+
+
+class GenerationRequest(BaseModel):
+    """Request model for voice generation."""
+
+    profile_id: str
+    text: str = Field(..., min_length=1, max_length=50000)
+    language: str = Field(default="en", pattern="^(zh|en|ja|ko|de|fr|ru|pt|es|it|he|ar|da|el|fi|hi|ms|nl|no|pl|sv|sw|tr)$")
+    seed: Optional[int] = Field(None, ge=0)
+    model_size: Optional[str] = Field(default="1.7B", pattern="^(1\\.7B|0\\.6B|1B|3B)$")
+    instruct: Optional[str] = Field(None, max_length=500)
+    engine: Optional[str] = Field(default="qwen", pattern="^(qwen|qwen_custom_voice|luxtts|chatterbox|chatterbox_turbo|tada|kokoro)$")
+    max_chunk_chars: int = Field(
+        default=800, ge=100, le=5000, description="Max characters per chunk for long text splitting"
+    )
+    crossfade_ms: int = Field(
+        default=50, ge=0, le=500, description="Crossfade duration in ms between chunks (0 for hard cut)"
+    )
+    normalize: bool = Field(default=True, description="Normalize output audio volume")
+    effects_chain: Optional[List["EffectConfig"]] = Field(
+        None, description="Effects chain to apply after generation (overrides profile default)"
+    )
+
+
+class GenerationResponse(BaseModel):
+    """Response model for voice generation."""
+
+    id: str
+    profile_id: str
+    text: str
+    language: str
+    audio_path: Optional[str] = None
+    duration: Optional[float] = None
+    seed: Optional[int] = None
+    instruct: Optional[str] = None
+    engine: Optional[str] = "qwen"
+    model_size: Optional[str] = None
+    status: str = "completed"
+    error: Optional[str] = None
+    is_favorited: bool = False
+    created_at: datetime
+    versions: Optional[List["GenerationVersionResponse"]] = None
+    active_version_id: Optional[str] = None
+
+    class Config:
+        from_attributes = True
+
+
+class HistoryQuery(BaseModel):
+    """Query model for generation history."""
+
+    profile_id: Optional[str] = None
+    search: Optional[str] = None
+    limit: int = Field(default=50, ge=1, le=100)
+    offset: int = Field(default=0, ge=0)
+
+
+class HistoryResponse(BaseModel):
+    """Response model for history entry (includes profile name)."""
+
+    id: str
+    profile_id: str
+    profile_name: str
+    text: str
+    language: str
+    audio_path: Optional[str] = None
+    duration: Optional[float] = None
+    seed: Optional[int] = None
+    instruct: Optional[str] = None
+    engine: Optional[str] = "qwen"
+    model_size: Optional[str] = None
+    status: str = "completed"
+    error: Optional[str] = None
+    is_favorited: bool = False
+    created_at: datetime
+    versions: Optional[List["GenerationVersionResponse"]] = None
+    active_version_id: Optional[str] = None
+
+    class Config:
+        from_attributes = True
+
+
+class HistoryListResponse(BaseModel):
+    """Response model for history list."""
+
+    items: List[HistoryResponse]
+    total: int
+
+
+class TranscriptionRequest(BaseModel):
+    """Request model for audio transcription."""
+
+    language: Optional[str] = Field(None, pattern="^(en|zh|ja|ko|de|fr|ru|pt|es|it)$")
+    model: Optional[str] = Field(None, pattern="^(base|small|medium|large|turbo)$")
+
+
+class TranscriptionResponse(BaseModel):
+    """Response model for transcription."""
+
+    text: str
+    duration: float
+
+
+class HealthResponse(BaseModel):
+    """Response model for health check."""
+
+    status: str
+    model_loaded: bool
+    model_downloaded: Optional[bool] = None  # Whether model is cached/downloaded
+    model_size: Optional[str] = None  # Current model size if loaded
+    gpu_available: bool
+    gpu_type: Optional[str] = None  # GPU type (CUDA, MPS, or None)
+    vram_used_mb: Optional[float] = None
+    backend_type: Optional[str] = None  # Backend type (mlx or pytorch)
+    backend_variant: Optional[str] = None  # Binary variant (cpu or cuda)
+    gpu_compatibility_warning: Optional[str] = None  # Warning if GPU arch unsupported
+
+
+class DirectoryCheck(BaseModel):
+    """Health status for a single directory."""
+
+    path: str
+    exists: bool
+    writable: bool
+    error: Optional[str] = None
+
+
+class FilesystemHealthResponse(BaseModel):
+    """Response model for filesystem health check."""
+
+    healthy: bool
+    disk_free_mb: Optional[float] = None
+    disk_total_mb: Optional[float] = None
+    directories: List[DirectoryCheck]
+
+
+class ModelStatus(BaseModel):
+    """Response model for model status."""
+
+    model_name: str
+    display_name: str
+    hf_repo_id: Optional[str] = None  # HuggingFace repository ID
+    downloaded: bool
+    downloading: bool = False  # True if download is in progress
+    size_mb: Optional[float] = None
+    loaded: bool = False
+
+
+class ModelStatusListResponse(BaseModel):
+    """Response model for model status list."""
+
+    models: List[ModelStatus]
+
+
+class ModelDownloadRequest(BaseModel):
+    """Request model for triggering model download."""
+
+    model_name: str
+
+
+class ModelMigrateRequest(BaseModel):
+    """Request model for migrating models to a new directory."""
+
+    destination: str
+
+
+class ActiveDownloadTask(BaseModel):
+    """Response model for active download task."""
+
+    model_name: str
+    status: str
+    started_at: datetime
+    error: Optional[str] = None
+    progress: Optional[float] = None  # 0-100 percentage
+    current: Optional[int] = None  # bytes downloaded
+    total: Optional[int] = None  # total bytes
+    filename: Optional[str] = None  # current file being downloaded
+
+
+class ActiveGenerationTask(BaseModel):
+    """Response model for active generation task."""
+
+    task_id: str
+    profile_id: str
+    text_preview: str
+    started_at: datetime
+
+
+class ActiveTasksResponse(BaseModel):
+    """Response model for active tasks."""
+
+    downloads: List[ActiveDownloadTask]
+    generations: List[ActiveGenerationTask]
+
+
+class AudioChannelCreate(BaseModel):
+    """Request model for creating an audio channel."""
+
+    name: str = Field(..., min_length=1, max_length=100)
+    device_ids: List[str] = Field(default_factory=list)
+
+
+class AudioChannelUpdate(BaseModel):
+    """Request model for updating an audio channel."""
+
+    name: Optional[str] = Field(None, min_length=1, max_length=100)
+    device_ids: Optional[List[str]] = None
+
+
+class AudioChannelResponse(BaseModel):
+    """Response model for audio channel."""
+
+    id: str
+    name: str
+    is_default: bool
+    device_ids: List[str]
+    created_at: datetime
+
+    class Config:
+        from_attributes = True
+
+
+class ChannelVoiceAssignment(BaseModel):
+    """Request model for assigning voices to a channel."""
+
+    profile_ids: List[str]
+
+
+class ProfileChannelAssignment(BaseModel):
+    """Request model for assigning channels to a profile."""
+
+    channel_ids: List[str]
+
+
+class StoryCreate(BaseModel):
+    """Request model for creating a story."""
+
+    name: str = Field(..., min_length=1, max_length=100)
+    description: Optional[str] = Field(None, max_length=500)
+
+
+class StoryResponse(BaseModel):
+    """Response model for story (list view)."""
+
+    id: str
+    name: str
+    description: Optional[str]
+    created_at: datetime
+    updated_at: datetime
+    item_count: int = 0
+
+    class Config:
+        from_attributes = True
+
+
+class StoryItemDetail(BaseModel):
+    """Detail model for story item with generation info."""
+
+    id: str
+    story_id: str
+    generation_id: str
+    version_id: Optional[str] = None
+    start_time_ms: int
+    track: int = 0
+    trim_start_ms: int = 0
+    trim_end_ms: int = 0
+    created_at: datetime
+    # Generation details
+    profile_id: str
+    profile_name: str
+    text: str
+    language: str
+    audio_path: str
+    duration: float
+    seed: Optional[int]
+    instruct: Optional[str]
+    generation_created_at: datetime
+    # Versions available for this generation
+    versions: Optional[List["GenerationVersionResponse"]] = None
+    active_version_id: Optional[str] = None
+
+    class Config:
+        from_attributes = True
+
+
+class StoryDetailResponse(BaseModel):
+    """Response model for story with items."""
+
+    id: str
+    name: str
+    description: Optional[str]
+    created_at: datetime
+    updated_at: datetime
+    items: List[StoryItemDetail] = []
+
+    class Config:
+        from_attributes = True
+
+
+class StoryItemCreate(BaseModel):
+    """Request model for adding a generation to a story."""
+
+    generation_id: str
+    start_time_ms: Optional[int] = None  # If not provided, will be calculated automatically
+    track: Optional[int] = 0  # Track number (0 = main track)
+
+
+class StoryItemUpdateTime(BaseModel):
+    """Request model for updating a story item's timecode."""
+
+    generation_id: str
+    start_time_ms: int = Field(..., ge=0)
+
+
+class StoryItemBatchUpdate(BaseModel):
+    """Request model for batch updating story item timecodes."""
+
+    updates: List[StoryItemUpdateTime]
+
+
+class StoryItemReorder(BaseModel):
+    """Request model for reordering story items."""
+
+    generation_ids: List[str] = Field(..., min_length=1)
+
+
+class StoryItemMove(BaseModel):
+    """Request model for moving a story item (position and/or track)."""
+
+    start_time_ms: int = Field(..., ge=0)
+    track: int = 0
+
+
+class StoryItemTrim(BaseModel):
+    """Request model for trimming a story item."""
+
+    trim_start_ms: int = Field(..., ge=0)
+    trim_end_ms: int = Field(..., ge=0)
+
+
+class StoryItemSplit(BaseModel):
+    """Request model for splitting a story item."""
+
+    split_time_ms: int = Field(..., ge=0)  # Time within the clip to split at (relative to clip start)
+
+
+class StoryItemVersionUpdate(BaseModel):
+    """Request model for setting a story item's pinned version."""
+
+    version_id: Optional[str] = None  # null = use generation default
+
+
+class EffectConfig(BaseModel):
+    """A single effect in an effects chain."""
+
+    type: str
+    enabled: bool = True
+    params: dict = Field(default_factory=dict)
+
+
+class EffectsChain(BaseModel):
+    """An ordered list of effects to apply."""
+
+    effects: List[EffectConfig] = Field(default_factory=list)
+
+
+class EffectPresetCreate(BaseModel):
+    """Request model for creating an effect preset."""
+
+    name: str = Field(..., min_length=1, max_length=100)
+    description: Optional[str] = Field(None, max_length=500)
+    effects_chain: List[EffectConfig]
+
+
+class EffectPresetUpdate(BaseModel):
+    """Request model for updating an effect preset."""
+
+    name: Optional[str] = Field(None, min_length=1, max_length=100)
+    description: Optional[str] = None
+    effects_chain: Optional[List[EffectConfig]] = None
+
+
+class EffectPresetResponse(BaseModel):
+    """Response model for effect preset."""
+
+    id: str
+    name: str
+    description: Optional[str] = None
+    effects_chain: List[EffectConfig]
+    is_builtin: bool = False
+    created_at: datetime
+
+    class Config:
+        from_attributes = True
+
+
+class GenerationVersionResponse(BaseModel):
+    """Response model for a generation version."""
+
+    id: str
+    generation_id: str
+    label: str
+    audio_path: str
+    effects_chain: Optional[List[EffectConfig]] = None
+    source_version_id: Optional[str] = None
+    is_default: bool
+    created_at: datetime
+
+    class Config:
+        from_attributes = True
+
+
+class ApplyEffectsRequest(BaseModel):
+    """Request to apply effects to an existing generation."""
+
+    effects_chain: List[EffectConfig]
+    source_version_id: Optional[str] = Field(
+        None, description="Version to use as source audio (defaults to clean/original)"
+    )
+    label: Optional[str] = Field(None, max_length=100, description="Label for this version (auto-generated if omitted)")
+    set_as_default: bool = Field(default=True, description="Set this version as the default")
+
+
+class ProfileEffectsUpdate(BaseModel):
+    """Request to update the default effects chain on a profile."""
+
+    effects_chain: Optional[List[EffectConfig]] = Field(None, description="Effects chain (null to remove)")
+
+
+class AvailableEffectParam(BaseModel):
+    """Description of a single effect parameter."""
+
+    default: float
+    min: float
+    max: float
+    step: float
+    description: str
+
+
+class AvailableEffect(BaseModel):
+    """Description of an available effect type."""
+
+    type: str
+    label: str
+    description: str
+    params: dict  # param_name -> AvailableEffectParam
+
+
+class AvailableEffectsResponse(BaseModel):
+    """Response listing all available effect types."""
+
+    effects: List[AvailableEffect]

backend/pyi_hooks/hook-scipy.stats._distn_infrastructure.py

@@ -0,0 +1,12 @@
+"""
+Force scipy.stats._distn_infrastructure to be bundled with its .py source file
+alongside the .pyc bytecode.
+
+The runtime hook in backend/pyi_rth_torch_compiler_disable.py patches this
+module's source at load time (the module has a `del obj` at line 369 that
+raises NameError under PyInstaller's frozen importer). That patch reads the
+source via loader.get_source(), which only works if the .py file was
+actually collected into the bundle.
+"""
+
+module_collection_mode = "pyz+py"

backend/pyi_hooks/hook-transformers.masking_utils.py

@@ -0,0 +1,11 @@
+"""
+Force transformers.masking_utils to be bundled with its .py source alongside
+the .pyc bytecode so the runtime hook in
+backend/pyi_rth_torch_compiler_disable.py can source-patch it.
+
+The patch forces the torch<2.6 code path, bypassing `with TransformGetItemToIndex()`
+which our torch._dynamo no-op stub can't implement for real — the real context
+manager uses dynamo graph transforms to avoid `.item()` calls inside vmap.
+"""
+
+module_collection_mode = "pyz+py"

backend/pyi_rth_numpy_compat.py

@@ -0,0 +1,95 @@
+"""
+PyInstaller runtime hook: numpy 2.x / torch ABI mismatch fix.
+
+Problem
+-------
+torch is compiled against numpy 1.x headers. numpy 2.x changed the version
+number returned by PyArray_GetNDArrayCVersion() (0x01000009 → 0x02000000),
+so torch's is_numpy_available() returns False and every torch.from_numpy()
+call raises:
+
+    RuntimeError: Numpy is not available
+
+This surfaces as:
+
+    ValueError: Unable to create tensor, you should probably activate
+    padding with 'padding=True'
+
+during TTS generation (EncodecFeatureExtractor → BatchFeature.convert_to_tensors).
+
+Fix
+---
+Runtime hooks execute after PyInstaller's FrozenImporter is registered, so
+frozen torch/numpy are importable here. We start a background thread that
+waits for torch to finish loading then wraps torch.from_numpy with a ctypes
+memmove fallback that bypasses the C-level numpy ABI check entirely.
+
+This approach works with any numpy version and is safer than binary-patching
+libtorch_python.dylib (which risks PyArray_Descr struct layout mismatches).
+"""
+
+import sys
+import threading
+
+
+def _patch_torch_from_numpy():
+    import time
+
+    for _ in range(7200):  # poll up to 360 s at 50 ms intervals
+        time.sleep(0.05)
+        torch = sys.modules.get("torch")
+        if torch is None or not hasattr(torch, "from_numpy"):
+            continue
+        if getattr(torch, "_vb_from_numpy_patched", False):
+            return
+        try:
+            import ctypes
+            import numpy as np
+
+            _orig = torch.from_numpy
+
+            # Explicit numpy → torch dtype map. Silent fallback to float32 on
+            # unknown dtypes would reinterpret the memcpy'd bytes as fp32 and
+            # silently corrupt data (e.g. fp16 tensors from some TTS engines),
+            # so we raise instead.
+            dtype_map = {
+                "float16": _t.float16,
+                "float32": _t.float32,
+                "float64": _t.float64,
+                "int8": _t.int8,
+                "int16": _t.int16,
+                "int32": _t.int32,
+                "int64": _t.int64,
+                "uint8": _t.uint8,
+                "bool": _t.bool,
+                "complex64": _t.complex64,
+                "complex128": _t.complex128,
+            }
+
+            def _safe_from_numpy(
+                arr, _orig=_orig, _c=ctypes, _np=np, _t=torch, _map=dtype_map
+            ):
+                try:
+                    return _orig(arr)
+                except RuntimeError:
+                    a = _np.ascontiguousarray(arr)
+                    key = str(a.dtype)
+                    if key not in _map:
+                        raise TypeError(
+                            f"pyi_rth_numpy_compat: unsupported numpy dtype "
+                            f"{key!r} in torch.from_numpy fallback; add an "
+                            f"explicit mapping rather than silently copying "
+                            f"bytes into the wrong dtype."
+                        )
+                    out = _t.empty(list(a.shape), dtype=_map[key])
+                    _c.memmove(out.data_ptr(), a.ctypes.data, a.nbytes)
+                    return out
+
+            torch.from_numpy = _safe_from_numpy
+            torch._vb_from_numpy_patched = True
+        except Exception:
+            pass
+        return
+
+
+threading.Thread(target=_patch_torch_from_numpy, daemon=True).start()

backend/pyi_rth_torch_compiler_disable.py

@@ -0,0 +1,540 @@
+"""
+PyInstaller runtime hook: stub torch._dynamo to a no-op module.
+
+Problem
+-------
+transformers triggers torch._dynamo import at module-load time (not just
+when torch.compile is called) via class-body decorators:
+
+    transformers/modeling_utils.py:1984
+        @torch._dynamo.allow_in_graph
+        class PreTrainedModel(...)
+
+    transformers/integrations/flex_attention.py:61
+        @torch.compiler.disable(recursive=False)
+        class WrappedFlexAttention...
+
+The attribute access triggers torch.__getattr__ -> importlib.import_module
+-> torch._dynamo -> torch._dynamo.utils imports torch._numpy ->
+torch._numpy._ndarray imports torch._numpy._ufuncs, which crashes under
+PyInstaller with:
+
+    File "torch/_numpy/_ufuncs.py", line 235, in <module>
+        vars()[name] = deco_binary_ufunc(ufunc)
+    NameError: name 'name' is not defined
+
+(The module-level `for name in _binary: vars()[name] = ...` pattern works
+in a regular venv but fails in the PyInstaller bundle. Root cause is in
+PyInstaller's importer / bytecode pipeline and not easily fixed upstream.)
+
+Surfaces as Kokoro failing to load when `from transformers import AlbertModel`
+trips the decorator chain.
+
+Fix
+---
+voicebox never uses torch.compile / torch._dynamo for inference, so we
+replace torch._dynamo with a no-op stub module before transformers is
+imported. Any attribute access on the stub returns a pass-through callable,
+so `@torch._dynamo.allow_in_graph`, `torch._dynamo.is_compiling()`,
+`torch._dynamo.mark_static_address(...)`, etc. all work.
+
+This hook is pure sys.modules manipulation — we deliberately do NOT import
+torch here. Runtime hooks run before the app starts and before
+pyi_rth_numpy_compat has had a chance to patch torch.from_numpy (it runs
+in a background thread, waiting for torch to appear in sys.modules).
+Eager-importing torch at hook time would trip the numpy ABI issue and
+kill the server process at startup.
+
+torch.compiler.disable does not need a separate stub: its implementation
+is effectively `import torch._dynamo; return torch._dynamo.disable(...)`,
+and since our stub is in sys.modules, that call resolves to our no-op
+_NoopDecorator pass-through.
+"""
+
+import os
+import sys
+import tempfile
+import types
+
+
+# Diagnostics — log hook activity to a file alongside the bundle so we can
+# see what's happening when the server is run as a sidecar (no stdout for
+# runtime hook prints). Safe no-op if the file can't be written.
+_DIAG_PATH = os.path.join(tempfile.gettempdir(), "voicebox_rt_hook.log")
+
+
+def _diag(msg: str) -> None:
+    try:
+        with open(_DIAG_PATH, "a", encoding="utf-8") as f:
+            f.write(msg + "\n")
+    except Exception:
+        pass
+
+
+_HOOK_VERSION = "v6-masking-utils-finder"
+_diag(f"=== runtime hook load @ pid={os.getpid()} version={_HOOK_VERSION} ===")
+
+
+class _NoopDecorator:
+    """Multi-role no-op: decorator, falsey predicate, and context manager.
+
+    Returned from calls like `torch._dynamo.disable()` (decorator),
+    `torch._dynamo.is_compiling()` (predicate used in `if not ...`), and
+    `with torch._dynamo._trace_wrapped_higher_order_op.TransformGetItemToIndex():`
+    (context manager used to scope an fx graph transformation).
+
+    By implementing __call__, __bool__, __enter__, __exit__, and __iter__ we
+    cover every use pattern we've seen transformers/torch use on a stubbed
+    object. Anything we haven't covered will raise a clearer error than a
+    silent wrong-result.
+    """
+
+    __slots__ = ()
+
+    def __call__(self, fn=None, *args, **kwargs):
+        return fn
+
+    def __bool__(self) -> bool:
+        return False
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        return False  # don't suppress exceptions
+
+    def __iter__(self):
+        return iter(())
+
+
+_noop_decorator_singleton = _NoopDecorator()
+
+
+def _noop_callable(*args, **kwargs):
+    # Direct-decorator use: @torch._dynamo.foo (no parens) — fn is positional
+    if len(args) == 1 and callable(args[0]) and not kwargs:
+        return args[0]
+    # Side-effect call with non-callable arg(s), e.g. mark_static_address(tensor)
+    return _noop_decorator_singleton
+
+
+class _NoopDynamoModule(types.ModuleType):
+    """Permissive stub: every attribute is a pass-through callable.
+
+    Covers attributes transformers hits at import time (allow_in_graph) and
+    runtime (is_compiling, mark_static_address, reset, disable, ...).
+
+    Dunder attributes (__file__, __spec__, __loader__, ...) raise
+    AttributeError so probes like inspect.getmodule() — which does
+    `hasattr(m, '__file__')` then `os.path.normpath(m.__file__)` — see the
+    module as having no source file and fall through to its normal
+    handling, instead of receiving a function and blowing up.
+    """
+
+    def __getattr__(self, name: str):
+        if name.startswith("__") and name.endswith("__"):
+            raise AttributeError(name)
+        return _noop_callable
+
+
+class _DynamoLoader:
+    """Loader used by _DynamoMetaPathFinder to materialise stub submodules."""
+
+    def create_module(self, spec):
+        return _NoopDynamoModule(spec.name)
+
+    def exec_module(self, module):
+        # Mark every stub submodule as a package so deeper submodule imports
+        # (`from torch._dynamo.X.Y import Z`) keep working.
+        module.__path__ = []
+
+
+class _DynamoMetaPathFinder:
+    """Resolve any `torch._dynamo.X[.Y...]` import to a no-op stub module.
+
+    Without this, `from torch._dynamo._trace_wrapped_higher_order_op import X`
+    fails even with torch._dynamo pre-populated in sys.modules — Python's
+    import machinery checks the parent's __path__ and then looks up the
+    child, and we need to provide both.
+    """
+
+    def find_spec(self, fullname, path=None, target=None):
+        if fullname == "torch._dynamo":
+            return None  # handled by the pre-populated sys.modules entry
+        if not fullname.startswith("torch._dynamo."):
+            return None
+        from importlib.machinery import ModuleSpec
+
+        return ModuleSpec(fullname, _DynamoLoader(), is_package=True)
+
+
+class _TransformersStubFinder:
+    """Replace specific transformers submodules with no-op stubs.
+
+    Two modules are targeted:
+
+    1. transformers.utils.auto_docstring
+       The real @auto_docstring decorator loads
+       transformers.models.auto.modeling_auto just to build example docstrings,
+       which drags in GenerationMixin -> candidate_generator -> sklearn.metrics
+       -> scipy.stats._distn_infrastructure and trips (2) below. Docstrings
+       aren't functional for inference, so a pass-through decorator is safe.
+
+    2. transformers.generation.candidate_generator
+       Imported at module scope by transformers.generation.utils. It does
+       `from sklearn.metrics import roc_curve` at module load, which triggers:
+
+            File "scipy/stats/_distn_infrastructure.py", line 369, in <module>
+            NameError: name 'obj' is not defined
+
+       This is a PyInstaller-specific module-load bug (same class as the
+       torch._numpy._ufuncs crash) where a module-level `for obj in [s for s
+       in dir() if ...]` loop evaluates to empty in the bundle, leaving `obj`
+       unbound before `del obj`.
+
+       The exports (AssistedCandidateGenerator, EarlyExitCandidateGenerator,
+       etc.) are speculative-decoding helpers voicebox's TTS engines do not
+       use; a no-op stub module satisfies the imports.
+    """
+
+    _STUBBED_MODULES = frozenset(
+        {
+            "transformers.utils.auto_docstring",
+            "transformers.generation.candidate_generator",
+        }
+    )
+
+    def find_spec(self, fullname, path=None, target=None):
+        if fullname not in self._STUBBED_MODULES:
+            return None
+        from importlib.machinery import ModuleSpec
+
+        return ModuleSpec(fullname, _NoopStubLoader(), is_package=False)
+
+
+class _NoopStubLoader:
+    def create_module(self, spec):
+        return _NoopDynamoModule(spec.name)
+
+    def exec_module(self, module):
+        # _NoopDynamoModule.__getattr__ already answers every non-dunder
+        # attribute with a pass-through callable, which satisfies
+        # `from stubbed_module import X` for any X.
+        pass
+
+
+def _patch_scipy_distn_source(source: str) -> str:
+    """Replace the unsafe `del obj` with a no-op that survives when obj is unbound.
+
+    Returns the input unchanged if the target line isn't found (e.g. scipy
+    version has changed).
+    """
+    target = "\ndel obj\n"
+    replacement = "\nglobals().pop('obj', None)\n"
+    if target in source:
+        return source.replace(target, replacement, 1)
+    return source
+
+
+def _patch_masking_utils_source(source: str) -> str:
+    """Force torch<2.6 code path in transformers.masking_utils.
+
+    The torch>=2.6 path uses `with TransformGetItemToIndex():` to allow
+    `.item()` calls inside vmap. That context manager is implemented via
+    torch._dynamo graph transforms, which our stub doesn't reproduce — it's
+    a no-op. The inner `_vmap_for_bhqkv` then crashes with:
+
+        RuntimeError: vmap: It looks like you're calling .item() on a Tensor.
+
+    Forcing the torch<2.6 flag off selects sdpa_mask_older_torch which uses
+    a different vmap pattern that does not hit .item() and does not need
+    TransformGetItemToIndex.
+    """
+    target = 'is_torch_greater_or_equal("2.6", accept_dev=True)'
+    # Find the specific line that assigns _is_torch_greater_or_equal_than_2_6
+    if "_is_torch_greater_or_equal_than_2_6 = " + target in source:
+        return source.replace(
+            "_is_torch_greater_or_equal_than_2_6 = " + target,
+            "_is_torch_greater_or_equal_than_2_6 = False",
+            1,
+        )
+    return source
+
+
+class _SourcePatchingFinder:
+    """Generic delegate-and-wrap meta-path finder that patches a module's
+    source before exec'ing.
+
+    Subclasses declare `target` (module fullname) and `patch` (str->str).
+    Requires the target module's .py source to be bundled (use a PyInstaller
+    hook setting module_collection_mode = "pyz+py").
+    """
+
+    target: str
+    patch_fn: callable = None
+
+    def find_spec(self, fullname, path=None, target=None):
+        if fullname != self.target:
+            return None
+        for finder in sys.meta_path:
+            if finder is self:
+                continue
+            find = getattr(finder, "find_spec", None)
+            if find is None:
+                continue
+            try:
+                real_spec = find(fullname, path, target)
+            except Exception:
+                continue
+            if real_spec is None or real_spec.loader is None:
+                continue
+            real_spec.loader = _SourcePatchLoader(real_spec.loader, self.patch_fn)
+            return real_spec
+        return None
+
+
+class _SourcePatchLoader:
+    """Delegate loader that reads source via get_source, applies a patch, and
+    compile/exec's the patched text into module.__dict__.
+    """
+
+    def __init__(self, inner, patch_fn):
+        self._inner = inner
+        self._patch_fn = patch_fn
+
+    def __getattr__(self, name):
+        return getattr(self._inner, name)
+
+    def create_module(self, spec):
+        return self._inner.create_module(spec)
+
+    def exec_module(self, module):
+        source = None
+        try:
+            source = self._inner.get_source(module.__name__)
+        except Exception as e:
+            _diag(f"[source-patch] get_source({module.__name__}) failed: {e!r}")
+
+        if not source:
+            _diag(
+                f"[source-patch] no source for {module.__name__}; "
+                "falling back to inner exec_module (patch NOT applied)"
+            )
+            self._inner.exec_module(module)
+            return
+
+        patched = self._patch_fn(source)
+        _diag(
+            f"[source-patch] {module.__name__}: "
+            f"patched={patched is not source}, len={len(patched)}"
+        )
+        spec = module.__spec__
+        if spec is not None and spec.submodule_search_locations is not None:
+            module.__path__ = spec.submodule_search_locations
+        filename = getattr(self._inner, "path", module.__name__)
+        exec(compile(patched, filename, "exec"), module.__dict__)
+        _diag(f"[source-patch] {module.__name__} OK")
+
+
+class _MaskingUtilsFinder(_SourcePatchingFinder):
+    target = "transformers.masking_utils"
+    patch_fn = staticmethod(_patch_masking_utils_source)
+
+
+class _ScipyDistnPatchingFinder:
+    """Delegate-and-wrap finder for scipy.stats._distn_infrastructure.
+
+    That module ends with:
+
+        for obj in [s for s in dir() if s.startswith('_doc_')]:
+            exec('del ' + obj)
+        del obj
+
+    In the PyInstaller bundle the list comprehension evaluates to empty
+    (module-level dir() under the frozen importer returns a different scope
+    than CPython's normal module-exec path — same class of bug as the
+    torch._numpy._ufuncs crash). The for loop body doesn't run, `obj` is
+    never bound, and the trailing `del obj` raises NameError at module load.
+
+    This kills every downstream module: librosa (needed by nearly every TTS
+    engine for mel filters) -> scipy.signal -> scipy.stats -> here.
+
+    Workaround: delegate to the real loader, but pre-bind `obj = None` in the
+    module namespace before its bytecode runs. If the for loop executes, each
+    iteration overwrites the sentinel via STORE_NAME (normal behaviour). If it
+    doesn't, `del obj` removes the sentinel and module load succeeds. The
+    `_doc_*` cleanup this line was meant to do is purely cosmetic — those vars
+    stay in the module namespace but nothing references them after this point.
+    """
+
+    _TARGET = "scipy.stats._distn_infrastructure"
+
+    def find_spec(self, fullname, path=None, target=None):
+        if fullname != self._TARGET:
+            return None
+        _diag(f"[scipy-finder] match: {fullname}, path={path!r}")
+        # Delegate to the other finders to locate the real spec
+        for finder in sys.meta_path:
+            if finder is self:
+                continue
+            find = getattr(finder, "find_spec", None)
+            if find is None:
+                continue
+            try:
+                real_spec = find(fullname, path, target)
+            except Exception as e:
+                _diag(f"[scipy-finder] inner finder {type(finder).__name__} raised: {e}")
+                continue
+            if real_spec is None:
+                continue
+            if real_spec.loader is None:
+                _diag(f"[scipy-finder] {type(finder).__name__} returned spec with loader=None")
+                continue
+            _diag(
+                f"[scipy-finder] wrapped loader from "
+                f"{type(finder).__name__} -> {type(real_spec.loader).__name__}"
+            )
+            real_spec.loader = _ScipyDistnPrebindLoader(real_spec.loader)
+            return real_spec
+        _diag("[scipy-finder] NO inner finder returned a spec")
+        return None
+
+
+class _ScipyDistnPrebindLoader:
+    """Thin wrapper that pre-binds `obj = None` before delegating to the
+    real PyInstaller loader.
+
+    Every other attribute/method delegates to the inner loader — PyiFrozenLoader
+    is a rich FileLoader/ExecutionLoader with get_code/get_source/get_filename/
+    is_package/get_resource_reader/etc., any of which Python's import machinery
+    or 3rd-party code may call on spec.loader. Forwarding via __getattr__
+    avoids breaking any of those paths (and preserves @_check_name contracts
+    because the decorated methods run on the inner instance where self.name
+    matches spec.name).
+    """
+
+    def __init__(self, inner):
+        self._inner = inner
+
+    def __getattr__(self, name):
+        # __getattr__ fires only for attrs not already on self, so delegate
+        # everything that isn't create_module/exec_module (or __getattr__/init).
+        return getattr(self._inner, name)
+
+    def create_module(self, spec):
+        return self._inner.create_module(spec)
+
+    def exec_module(self, module):
+        # Compile scipy's module source with the problematic line patched.
+        #
+        # The real module ends with:
+        #     for obj in [s for s in dir() if s.startswith('_doc_')]:
+        #         exec('del ' + obj)
+        #     del obj
+        #
+        # Under PyInstaller's frozen importer, `del obj` raises NameError
+        # even when we pre-populate module.__dict__['obj'] — the pre-compiled
+        # .pyc bytecode interacts with the frame setup differently than a
+        # fresh compile() from source. Easiest robust fix: read the source
+        # and replace `del obj` with a safe variant before compiling.
+        #
+        # Requires the .py source to be bundled alongside the .pyc — see
+        # backend/pyi_hooks/hook-scipy.stats._distn_infrastructure.py.
+        source = None
+        try:
+            source = self._inner.get_source(module.__name__)
+        except Exception as e:
+            _diag(f"[scipy-loader] get_source failed: {e!r}")
+
+        if source:
+            patched = _patch_scipy_distn_source(source)
+            _diag(
+                f"[scipy-loader] source-patch path: patched={patched is not source}, "
+                f"len={len(patched)}"
+            )
+            spec = module.__spec__
+            if spec is not None and spec.submodule_search_locations is not None:
+                module.__path__ = spec.submodule_search_locations
+            filename = getattr(self._inner, "path", module.__name__)
+            bytecode = compile(patched, filename, "exec")
+            try:
+                exec(bytecode, module.__dict__)
+            except Exception as e:
+                _diag(f"[scipy-loader] patched exec raised {type(e).__name__}: {e!r}")
+                raise
+            _diag(f"[scipy-loader] exec_module {module.__name__} OK (source-patched)")
+            return
+
+        # No source available — fall back to the pre-bind approach. This is
+        # best-effort; if the frozen .pyc really does see a different `obj`
+        # slot, this will still crash, but we've done all we can without
+        # source.
+        _diag("[scipy-loader] no source available; falling back to pre-bind")
+        module.__dict__["obj"] = None
+        self._inner.exec_module(module)
+
+
+def _install_dynamo_stub() -> None:
+    stub = _NoopDynamoModule("torch._dynamo")
+    # Mark as a package so `from torch._dynamo.X import Y` imports work
+    # (Python's import machinery checks parent.__path__ before looking up
+    # the child).
+    stub.__path__ = []
+    # torch._dynamo.config is accessed as a nested attribute namespace
+    # (e.g. `torch._dynamo.config.capture_scalar_outputs = True`), so use
+    # a permissive module so any attr read returns a no-op and sets succeed.
+    stub.config = _NoopDynamoModule("torch._dynamo.config")
+    stub.config.__path__ = []
+    sys.modules["torch._dynamo"] = stub
+    sys.modules["torch._dynamo.config"] = stub.config
+
+    # Finders:
+    #  - torch._dynamo.* submodules -> no-op stubs
+    #  - transformers.utils.auto_docstring and
+    #    transformers.generation.candidate_generator -> no-op stubs (both
+    #    paths reach sklearn -> scipy.stats which trips a separate crash)
+    #  - scipy.stats._distn_infrastructure -> real load with `obj` pre-bound,
+    #    so librosa -> scipy.signal -> scipy.stats loads cleanly
+    for _FinderCls in (
+        _DynamoMetaPathFinder,
+        _TransformersStubFinder,
+        _ScipyDistnPatchingFinder,
+        _MaskingUtilsFinder,
+    ):
+        try:
+            sys.meta_path.insert(0, _FinderCls())
+            _diag(f"installed finder: {_FinderCls.__name__}")
+        except Exception as e:
+            _diag(f"FAILED to install {_FinderCls.__name__}: {e!r}")
+    _diag(
+        "final sys.meta_path head: "
+        + ", ".join(type(f).__name__ for f in sys.meta_path[:6])
+    )
+
+    # If torch is already imported, also set the attribute on the package so
+    # `torch._dynamo` resolves to our stub without triggering torch.__getattr__
+    # (which would lazy-import the real module and crash).
+    torch_mod = sys.modules.get("torch")
+    if torch_mod is not None:
+        torch_mod._dynamo = stub
+
+
+try:
+    _install_dynamo_stub()
+except Exception as _e:
+    # Best effort. If this fails the original NameError will surface when
+    # transformers imports — no worse than not patching at all.
+    _diag(f"_install_dynamo_stub FAILED: {_e!r}")
+
+# NOTE: we deliberately do NOT import torch or torch.compiler here.
+# Runtime hooks run before the app starts and before pyi_rth_numpy_compat
+# has had a chance to patch torch.from_numpy (it runs in a background
+# thread, waiting for torch to appear in sys.modules). Importing torch
+# eagerly at hook time would trip the numpy ABI issue and kill the
+# server process at startup.
+#
+# torch.compiler.disable does not need an explicit stub: its
+# implementation is effectively `import torch._dynamo; return
+# torch._dynamo.disable(fn, recursive, reason=reason)`, and since our
+# stub is installed in sys.modules, that call resolves to our no-op
+# _NoopDecorator pass-through.

backend/pyproject.toml

@@ -0,0 +1,83 @@
+[project]
+name = "voicebox-backend"
+version = "0.2.3"
+requires-python = ">=3.12"
+
+# ---------------------------------------------------------------------------
+# Ruff – linter + formatter
+# ---------------------------------------------------------------------------
+
+[tool.ruff]
+target-version = "py312"
+line-length = 120
+src = ["."]
+
+# Files/dirs to skip entirely.
+extend-exclude = [
+    "voicebox-server.spec",
+    "build_binary.py",
+]
+
+[tool.ruff.lint]
+select = [
+    "F",     # pyflakes
+    "E",     # pycodestyle errors
+    "W",     # pycodestyle warnings
+    "I",     # isort
+    "N",     # pep8-naming
+    "UP",    # pyupgrade (modernize syntax for 3.12)
+    "B",     # flake8-bugbear
+    "A",     # flake8-builtins (shadowing built-in names)
+    "SIM",   # flake8-simplify
+    "T20",   # flake8-print (flag print() calls)
+    "RET",   # flake8-return
+    "PIE",   # misc lints
+    "PT",    # flake8-pytest-style
+    "RUF",   # ruff-specific rules
+    "ERA",   # commented-out code detection
+    "FIX",   # flag TODO/FIXME/HACK/XXX for review
+]
+
+ignore = [
+    # Allow print() in existing code -- remove items from this list as files
+    # are migrated to logging during the refactor.
+    "T201",   # print() found
+
+    # These conflict with the formatter or are too noisy during migration:
+    "E501",   # line too long (formatter handles this)
+    "RET504", # unnecessary assignment before return
+    "SIM108", # use ternary operator (sometimes less readable)
+    "B008",   # function call in default argument (FastAPI Depends() pattern)
+    "UP007",  # use X | Y for union (auto-fixed by UP, but noisy on big diffs)
+]
+
+# Per-file rule overrides.
+[tool.ruff.lint.per-file-ignores]
+# Tests can use assert, print, and magic values freely.
+"tests/**" = ["S101", "T201", "PLR2004", "ERA001"]
+# __init__.py re-exports are expected to have unused imports.
+"**/__init__.py" = ["F401"]
+# Entry points and scripts legitimately use print.
+"server.py" = ["T201"]
+"main.py" = ["T201"]
+# AMD GPU env vars must be set before torch import.
+"app.py" = ["E402"]
+
+[tool.ruff.lint.isort]
+known-first-party = ["backend"]
+# Group "from backend.*" imports into the first-party section.
+force-single-line = false
+combine-as-imports = true
+
+[tool.ruff.format]
+quote-style = "double"
+indent-style = "space"
+docstring-code-format = true
+
+# ---------------------------------------------------------------------------
+# pytest
+# ---------------------------------------------------------------------------
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+asyncio_mode = "auto"

backend/requirements-mlx.txt

@@ -0,0 +1,22 @@
+# MLX-specific dependencies (Apple Silicon only)
+# These should only be installed on aarch64-apple-darwin platforms
+
+mlx>=0.30.0
+
+# miniaudio is a runtime dep of mlx-audio's STT path (mlx_audio.stt).
+# mlx-audio itself is installed --no-deps (see comment below), so we
+# must list miniaudio explicitly here or transcription fails on fresh
+# M1 installs with `ModuleNotFoundError: miniaudio` (issue #505).
+miniaudio>=1.59
+
+# NOTE: mlx-audio is intentionally not listed here. From 0.3.1 onward it
+# declares `transformers==5.0.0rc3` / `>=5.0.0`, which conflicts with the
+# `transformers<=4.57.6` cap in requirements.txt and breaks CI's clean
+# resolver. The mlx-audio API surface we use (mlx_audio.tts.load,
+# mlx_audio.stt.load) works fine on transformers 4.57.x in practice.
+#
+# Install it via `pip install --no-deps mlx-audio==0.4.1` after this file
+# (see .github/workflows/release.yml). Most other mlx-audio runtime deps
+# (huggingface_hub, librosa, mlx-lm, numba, numpy, protobuf, pyloudnorm,
+# sounddevice, tqdm) are already in requirements.txt or pulled in by
+# other engines.

backend/requirements.txt

@@ -0,0 +1,67 @@
+# FastAPI and server
+fastapi>=0.109.0
+uvicorn[standard]>=0.27.0
+pydantic>=2.5.0
+
+# Database
+sqlalchemy>=2.0.0
+alembic>=1.13.0
+
+# ML models
+torch>=2.2.0
+transformers>=4.36.0,<=4.57.6
+accelerate>=0.26.0
+huggingface_hub>=0.20.0
+qwen-tts>=0.0.5
+
+# LuxTTS (voice cloning engine)
+# piper-phonemize needs custom index (no PyPI wheels)
+--find-links https://k2-fsa.github.io/icefall/piper_phonemize.html
+# linacodec is a git-only dep of Zipvoice (uv-only source, pip can't resolve it)
+linacodec @ git+https://github.com/ysharma3501/LinaCodec.git
+Zipvoice @ git+https://github.com/ysharma3501/LuxTTS.git
+
+# Chatterbox TTS sub-dependencies (chatterbox-tts itself is installed
+# --no-deps in the setup script because it pins numpy<1.26 / torch==2.6
+# which are incompatible with Python 3.12+)
+conformer>=0.3.2
+diffusers>=0.29.0
+omegaconf
+pykakasi
+resemble-perth>=1.0.1
+s3tokenizer
+spacy-pkuseg
+pyloudnorm
+
+# HumeAI TADA sub-dependencies (hume-tada itself is installed
+# --no-deps in the setup script because it pins torch>=2.7,<2.8.
+# descript-audio-codec is NOT installed — it pulls onnx/tensorboard
+# via descript-audiotools.  A lightweight shim in utils/dac_shim.py
+# provides the only class TADA uses: Snake1d.)
+torchaudio
+
+# Kokoro TTS (lightweight 82M-param engine)
+kokoro>=0.9.4
+misaki[en,ja,zh]>=0.9.4
+# spacy model for misaki English G2P — must be pre-installed or misaki
+# tries spacy.cli.download() at runtime which crashes frozen builds
+en_core_web_sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.8.0/en_core_web_sm-3.8.0-py3-none-any.whl
+# fugashi (pulled in by misaki[ja]) needs a MeCab dictionary on disk.
+# unidic-lite ships one inside the wheel (~50MB); the full `unidic` package
+# requires `python -m unidic download` (~526MB) which breaks frozen builds
+# for the same reason en_core_web_sm does.
+unidic-lite>=1.0.8
+
+# Audio processing
+librosa>=0.10.0
+soundfile>=0.12.0
+numpy>=1.24.0,<2.0
+numba>=0.60.0,<0.61.0
+pedalboard>=0.9.0
+
+# HTTP client (for CUDA backend download)
+httpx>=0.27.0
+
+# Utilities
+python-multipart>=0.0.6
+Pillow>=10.0.0

backend/routes/__init__.py

@@ -0,0 +1,32 @@
+"""Route registration for the voicebox API."""
+
+from fastapi import FastAPI
+
+
+def register_routers(app: FastAPI) -> None:
+    """Include all domain routers on the application."""
+    from .health import router as health_router
+    from .profiles import router as profiles_router
+    from .channels import router as channels_router
+    from .generations import router as generations_router
+    from .history import router as history_router
+    from .transcription import router as transcription_router
+    from .stories import router as stories_router
+    from .effects import router as effects_router
+    from .audio import router as audio_router
+    from .models import router as models_router
+    from .tasks import router as tasks_router
+    from .cuda import router as cuda_router
+
+    app.include_router(health_router)
+    app.include_router(profiles_router)
+    app.include_router(channels_router)
+    app.include_router(generations_router)
+    app.include_router(history_router)
+    app.include_router(transcription_router)
+    app.include_router(stories_router)
+    app.include_router(effects_router)
+    app.include_router(audio_router)
+    app.include_router(models_router)
+    app.include_router(tasks_router)
+    app.include_router(cuda_router)

backend/routes/audio.py

@@ -0,0 +1,69 @@
+"""Audio file serving endpoints."""
+
+from fastapi import APIRouter, Depends, HTTPException
+from fastapi.responses import FileResponse
+from sqlalchemy.orm import Session
+
+from .. import config, models
+from ..services import history
+from ..database import get_db
+
+router = APIRouter()
+
+
+@router.get("/audio/version/{version_id}")
+async def get_version_audio(version_id: str, db: Session = Depends(get_db)):
+    """Serve audio for a specific version."""
+    from ..services import versions as versions_mod
+
+    version = versions_mod.get_version(version_id, db)
+    if not version:
+        raise HTTPException(status_code=404, detail="Version not found")
+
+    audio_path = config.resolve_storage_path(version.audio_path)
+    if audio_path is None or not audio_path.exists():
+        raise HTTPException(status_code=404, detail="Audio file not found")
+
+    return FileResponse(
+        audio_path,
+        media_type="audio/wav",
+        filename=f"generation_{version.generation_id}_{version.label}.wav",
+    )
+
+
+@router.get("/audio/{generation_id}")
+async def get_audio(generation_id: str, db: Session = Depends(get_db)):
+    """Serve generated audio file (serves the default version)."""
+    generation = await history.get_generation(generation_id, db)
+    if not generation:
+        raise HTTPException(status_code=404, detail="Generation not found")
+
+    audio_path = config.resolve_storage_path(generation.audio_path)
+    if audio_path is None or not audio_path.exists():
+        raise HTTPException(status_code=404, detail="Audio file not found")
+
+    return FileResponse(
+        audio_path,
+        media_type="audio/wav",
+        filename=f"generation_{generation_id}.wav",
+    )
+
+
+@router.get("/samples/{sample_id}")
+async def get_sample_audio(sample_id: str, db: Session = Depends(get_db)):
+    """Serve profile sample audio file."""
+    from ..database import ProfileSample as DBProfileSample
+
+    sample = db.query(DBProfileSample).filter_by(id=sample_id).first()
+    if not sample:
+        raise HTTPException(status_code=404, detail="Sample not found")
+
+    audio_path = config.resolve_storage_path(sample.audio_path)
+    if audio_path is None or not audio_path.exists():
+        raise HTTPException(status_code=404, detail="Audio file not found")
+
+    return FileResponse(
+        audio_path,
+        media_type="audio/wav",
+        filename=f"sample_{sample_id}.wav",
+    )

backend/routes/channels.py

@@ -0,0 +1,98 @@
+"""Audio channel endpoints."""
+
+from fastapi import APIRouter, Depends, HTTPException
+from sqlalchemy.orm import Session
+
+from .. import models
+from ..services import channels
+from ..database import get_db
+
+router = APIRouter()
+
+
+@router.get("/channels", response_model=list[models.AudioChannelResponse])
+async def list_channels(db: Session = Depends(get_db)):
+    """List all audio channels."""
+    return await channels.list_channels(db)
+
+
+@router.post("/channels", response_model=models.AudioChannelResponse)
+async def create_channel(
+    data: models.AudioChannelCreate,
+    db: Session = Depends(get_db),
+):
+    """Create a new audio channel."""
+    try:
+        return await channels.create_channel(data, db)
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.get("/channels/{channel_id}", response_model=models.AudioChannelResponse)
+async def get_channel(
+    channel_id: str,
+    db: Session = Depends(get_db),
+):
+    """Get an audio channel by ID."""
+    channel = await channels.get_channel(channel_id, db)
+    if not channel:
+        raise HTTPException(status_code=404, detail="Channel not found")
+    return channel
+
+
+@router.put("/channels/{channel_id}", response_model=models.AudioChannelResponse)
+async def update_channel(
+    channel_id: str,
+    data: models.AudioChannelUpdate,
+    db: Session = Depends(get_db),
+):
+    """Update an audio channel."""
+    try:
+        channel = await channels.update_channel(channel_id, data, db)
+        if not channel:
+            raise HTTPException(status_code=404, detail="Channel not found")
+        return channel
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.delete("/channels/{channel_id}")
+async def delete_channel(
+    channel_id: str,
+    db: Session = Depends(get_db),
+):
+    """Delete an audio channel."""
+    try:
+        success = await channels.delete_channel(channel_id, db)
+        if not success:
+            raise HTTPException(status_code=404, detail="Channel not found")
+        return {"message": "Channel deleted successfully"}
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.get("/channels/{channel_id}/voices")
+async def get_channel_voices(
+    channel_id: str,
+    db: Session = Depends(get_db),
+):
+    """Get list of profile IDs assigned to a channel."""
+    try:
+        profile_ids = await channels.get_channel_voices(channel_id, db)
+        return {"profile_ids": profile_ids}
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.put("/channels/{channel_id}/voices")
+async def set_channel_voices(
+    channel_id: str,
+    data: models.ChannelVoiceAssignment,
+    db: Session = Depends(get_db),
+):
+    """Set which voices are assigned to a channel."""
+    try:
+        await channels.set_channel_voices(channel_id, data, db)
+        return {"message": "Channel voices updated successfully"}
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))

backend/routes/cuda.py

@@ -0,0 +1,82 @@
+"""CUDA backend management endpoints."""
+
+import logging
+
+from fastapi import APIRouter, HTTPException
+from fastapi.responses import StreamingResponse
+
+from ..services.task_queue import create_background_task
+from ..utils.progress import get_progress_manager
+
+router = APIRouter()
+
+logger = logging.getLogger(__name__)
+
+
+@router.get("/backend/cuda-status")
+async def get_cuda_status():
+    """Get CUDA backend download/availability status."""
+    from ..services import cuda
+
+    return cuda.get_cuda_status()
+
+
+@router.post("/backend/download-cuda")
+async def download_cuda_backend():
+    """Download the CUDA backend binary."""
+    from ..services import cuda
+
+    if cuda.get_cuda_binary_path() is not None:
+        raise HTTPException(status_code=409, detail="CUDA backend already downloaded")
+
+    progress_manager = get_progress_manager()
+    existing = progress_manager.get_progress(cuda.PROGRESS_KEY)
+    if existing and existing.get("status") == "downloading":
+        raise HTTPException(status_code=409, detail="CUDA backend download already in progress")
+
+    async def _download():
+        try:
+            await cuda.download_cuda_binary()
+        except Exception as e:
+            logger.error("CUDA download failed: %s", e)
+
+    create_background_task(_download())
+    return {"message": "CUDA backend download started", "progress_key": "cuda-backend"}
+
+
+@router.delete("/backend/cuda")
+async def delete_cuda_backend():
+    """Delete the downloaded CUDA backend binary."""
+    from ..services import cuda
+
+    if cuda.is_cuda_active():
+        raise HTTPException(
+            status_code=409,
+            detail="Cannot delete CUDA backend while it is active. Switch to CPU first.",
+        )
+
+    deleted = await cuda.delete_cuda_binary()
+    if not deleted:
+        raise HTTPException(status_code=404, detail="No CUDA backend found to delete")
+
+    return {"message": "CUDA backend deleted"}
+
+
+@router.get("/backend/cuda-progress")
+async def get_cuda_download_progress():
+    """Get CUDA backend download progress via Server-Sent Events."""
+    progress_manager = get_progress_manager()
+
+    async def event_generator():
+        async for event in progress_manager.subscribe("cuda-backend"):
+            yield event
+
+    return StreamingResponse(
+        event_generator(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "X-Accel-Buffering": "no",
+        },
+    )

backend/routes/effects.py

@@ -0,0 +1,262 @@
+"""Effects presets and generation version endpoints."""
+
+import asyncio
+import io
+import uuid
+
+from fastapi import APIRouter, Depends, HTTPException
+from fastapi.responses import StreamingResponse
+from sqlalchemy.orm import Session
+
+from .. import config, models
+from ..services import history
+from ..database import Generation as DBGeneration, get_db
+
+router = APIRouter()
+
+
+@router.post("/effects/preview/{generation_id}")
+async def preview_effects(
+    generation_id: str,
+    data: models.ApplyEffectsRequest,
+    db: Session = Depends(get_db),
+):
+    """Apply effects to a generation's clean audio and stream back without saving."""
+    gen = db.query(DBGeneration).filter_by(id=generation_id).first()
+    if not gen:
+        raise HTTPException(status_code=404, detail="Generation not found")
+    if (gen.status or "completed") != "completed":
+        raise HTTPException(status_code=400, detail="Generation is not completed")
+
+    from ..services import versions as versions_mod
+    from ..utils.effects import apply_effects, validate_effects_chain
+    from ..utils.audio import load_audio
+
+    chain_dicts = [e.model_dump() for e in data.effects_chain]
+    error = validate_effects_chain(chain_dicts)
+    if error:
+        raise HTTPException(status_code=400, detail=error)
+
+    all_versions = versions_mod.list_versions(generation_id, db)
+    clean_version = next((v for v in all_versions if v.effects_chain is None), None)
+    source_path = clean_version.audio_path if clean_version else gen.audio_path
+    resolved_source_path = config.resolve_storage_path(source_path)
+    if resolved_source_path is None or not resolved_source_path.exists():
+        raise HTTPException(status_code=404, detail="Source audio file not found")
+
+    audio, sample_rate = await asyncio.to_thread(load_audio, str(resolved_source_path))
+    processed = await asyncio.to_thread(apply_effects, audio, sample_rate, chain_dicts)
+
+    import soundfile as sf
+
+    buf = io.BytesIO()
+    await asyncio.to_thread(lambda: sf.write(buf, processed, sample_rate, format="WAV"))
+    buf.seek(0)
+
+    return StreamingResponse(
+        buf,
+        media_type="audio/wav",
+        headers={
+            "Content-Disposition": f'inline; filename="preview_{generation_id}.wav"',
+            "Cache-Control": "no-cache, no-store",
+        },
+    )
+
+
+@router.get("/effects/available", response_model=models.AvailableEffectsResponse)
+async def get_available_effects():
+    """List all available effect types with parameter definitions."""
+    from ..utils.effects import get_available_effects as _get_effects
+
+    return models.AvailableEffectsResponse(effects=[models.AvailableEffect(**e) for e in _get_effects()])
+
+
+@router.get("/effects/presets", response_model=list[models.EffectPresetResponse])
+async def list_effect_presets(db: Session = Depends(get_db)):
+    """List all effect presets (built-in + user-created)."""
+    from ..services import effects as effects_mod
+
+    return effects_mod.list_presets(db)
+
+
+@router.get("/effects/presets/{preset_id}", response_model=models.EffectPresetResponse)
+async def get_effect_preset(preset_id: str, db: Session = Depends(get_db)):
+    """Get a specific effect preset."""
+    from ..services import effects as effects_mod
+
+    preset = effects_mod.get_preset(preset_id, db)
+    if not preset:
+        raise HTTPException(status_code=404, detail="Preset not found")
+    return preset
+
+
+@router.post("/effects/presets", response_model=models.EffectPresetResponse)
+async def create_effect_preset(
+    data: models.EffectPresetCreate,
+    db: Session = Depends(get_db),
+):
+    """Create a new effect preset."""
+    from ..services import effects as effects_mod
+
+    try:
+        return effects_mod.create_preset(data, db)
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.put("/effects/presets/{preset_id}", response_model=models.EffectPresetResponse)
+async def update_effect_preset(
+    preset_id: str,
+    data: models.EffectPresetUpdate,
+    db: Session = Depends(get_db),
+):
+    """Update an effect preset."""
+    from ..services import effects as effects_mod
+
+    try:
+        result = effects_mod.update_preset(preset_id, data, db)
+        if not result:
+            raise HTTPException(status_code=404, detail="Preset not found")
+        return result
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.delete("/effects/presets/{preset_id}")
+async def delete_effect_preset(preset_id: str, db: Session = Depends(get_db)):
+    """Delete a user effect preset."""
+    from ..services import effects as effects_mod
+
+    try:
+        if not effects_mod.delete_preset(preset_id, db):
+            raise HTTPException(status_code=404, detail="Preset not found")
+        return {"status": "deleted"}
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.get(
+    "/generations/{generation_id}/versions",
+    response_model=list[models.GenerationVersionResponse],
+)
+async def list_generation_versions(
+    generation_id: str,
+    db: Session = Depends(get_db),
+):
+    """List all versions for a generation."""
+    gen = await history.get_generation(generation_id, db)
+    if not gen:
+        raise HTTPException(status_code=404, detail="Generation not found")
+
+    from ..services import versions as versions_mod
+
+    return versions_mod.list_versions(generation_id, db)
+
+
+@router.post(
+    "/generations/{generation_id}/versions/apply-effects",
+    response_model=models.GenerationVersionResponse,
+)
+async def apply_effects_to_generation(
+    generation_id: str,
+    data: models.ApplyEffectsRequest,
+    db: Session = Depends(get_db),
+):
+    """Apply an effects chain to an existing generation, creating a new version."""
+    gen = db.query(DBGeneration).filter_by(id=generation_id).first()
+    if not gen:
+        raise HTTPException(status_code=404, detail="Generation not found")
+    if (gen.status or "completed") != "completed":
+        raise HTTPException(status_code=400, detail="Generation is not completed")
+
+    from ..services import versions as versions_mod
+    from ..utils.effects import apply_effects, validate_effects_chain
+    from ..utils.audio import load_audio, save_audio
+
+    chain_dicts = [e.model_dump() for e in data.effects_chain]
+    error = validate_effects_chain(chain_dicts)
+    if error:
+        raise HTTPException(status_code=400, detail=error)
+
+    all_versions = versions_mod.list_versions(generation_id, db)
+    source_version_id = data.source_version_id
+    if source_version_id:
+        source_version = next((v for v in all_versions if v.id == source_version_id), None)
+        if not source_version:
+            raise HTTPException(status_code=404, detail="Source version not found")
+        source_path = source_version.audio_path
+    else:
+        clean_version = next((v for v in all_versions if v.effects_chain is None), None)
+        if not clean_version:
+            source_path = gen.audio_path
+        else:
+            source_path = clean_version.audio_path
+            source_version_id = clean_version.id
+
+    resolved_source_path = config.resolve_storage_path(source_path)
+    if resolved_source_path is None or not resolved_source_path.exists():
+        raise HTTPException(status_code=404, detail="Source audio file not found")
+
+    audio, sample_rate = await asyncio.to_thread(load_audio, str(resolved_source_path))
+    processed_audio = await asyncio.to_thread(apply_effects, audio, sample_rate, chain_dicts)
+
+    version_id = str(uuid.uuid4())
+    processed_path = config.get_generations_dir() / f"{generation_id}_{version_id[:8]}.wav"
+    await asyncio.to_thread(save_audio, processed_audio, str(processed_path), sample_rate)
+
+    label = data.label or f"version-{len(all_versions) + 1}"
+
+    version = versions_mod.create_version(
+        generation_id=generation_id,
+        label=label,
+        audio_path=config.to_storage_path(processed_path),
+        db=db,
+        effects_chain=chain_dicts,
+        is_default=data.set_as_default,
+        source_version_id=source_version_id,
+    )
+
+    return version
+
+
+@router.put(
+    "/generations/{generation_id}/versions/{version_id}/set-default",
+    response_model=models.GenerationVersionResponse,
+)
+async def set_default_version(
+    generation_id: str,
+    version_id: str,
+    db: Session = Depends(get_db),
+):
+    """Set a specific version as the default for a generation."""
+    from ..services import versions as versions_mod
+
+    version = versions_mod.get_version(version_id, db)
+    if not version or version.generation_id != generation_id:
+        raise HTTPException(status_code=404, detail="Version not found")
+
+    result = versions_mod.set_default_version(version_id, db)
+    if not result:
+        raise HTTPException(status_code=404, detail="Version not found")
+    return result
+
+
+@router.delete("/generations/{generation_id}/versions/{version_id}")
+async def delete_generation_version(
+    generation_id: str,
+    version_id: str,
+    db: Session = Depends(get_db),
+):
+    """Delete a version. Cannot delete the last remaining version."""
+    from ..services import versions as versions_mod
+
+    version = versions_mod.get_version(version_id, db)
+    if not version or version.generation_id != generation_id:
+        raise HTTPException(status_code=404, detail="Version not found")
+
+    if not versions_mod.delete_version(version_id, db):
+        raise HTTPException(
+            status_code=400,
+            detail="Cannot delete the last remaining version",
+        )
+    return {"status": "deleted"}

backend/routes/generations.py

@@ -0,0 +1,345 @@
+"""TTS generation endpoints."""
+
+import asyncio
+import logging
+import uuid
+
+from fastapi import APIRouter, Depends, HTTPException
+from fastapi.responses import StreamingResponse
+from sqlalchemy.orm import Session
+
+logger = logging.getLogger(__name__)
+
+from .. import models
+from ..services import history, profiles, tts
+from ..database import Generation as DBGeneration, VoiceProfile as DBVoiceProfile, get_db
+from ..services.generation import run_generation
+from ..services.task_queue import cancel_generation as cancel_generation_job, enqueue_generation
+from ..utils.tasks import get_task_manager
+
+router = APIRouter()
+
+
+def _resolve_generation_engine(data: models.GenerationRequest, profile) -> str:
+    return data.engine or getattr(profile, "default_engine", None) or getattr(profile, "preset_engine", None) or "qwen"
+
+
+@router.post("/generate", response_model=models.GenerationResponse)
+async def generate_speech(
+    data: models.GenerationRequest,
+    db: Session = Depends(get_db),
+):
+    """Generate speech from text using a voice profile."""
+    task_manager = get_task_manager()
+    generation_id = str(uuid.uuid4())
+
+    profile = await profiles.get_profile(data.profile_id, db)
+    if not profile:
+        raise HTTPException(status_code=404, detail="Profile not found")
+
+    from ..backends import engine_has_model_sizes
+
+    engine = _resolve_generation_engine(data, profile)
+    try:
+        profiles.validate_profile_engine(profile, engine)
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+    model_size = (data.model_size or "1.7B") if engine_has_model_sizes(engine) else None
+
+    generation = await history.create_generation(
+        profile_id=data.profile_id,
+        text=data.text,
+        language=data.language,
+        audio_path="",
+        duration=0,
+        seed=data.seed,
+        db=db,
+        instruct=data.instruct,
+        generation_id=generation_id,
+        status="generating",
+        engine=engine,
+        model_size=model_size if engine_has_model_sizes(engine) else None,
+    )
+
+    task_manager.start_generation(
+        task_id=generation_id,
+        profile_id=data.profile_id,
+        text=data.text,
+    )
+
+    effects_chain_config = None
+    if data.effects_chain is not None:
+        effects_chain_config = [e.model_dump() for e in data.effects_chain]
+    else:
+        import json as _json
+
+        profile_obj = db.query(DBVoiceProfile).filter_by(id=data.profile_id).first()
+        if profile_obj and profile_obj.effects_chain:
+            try:
+                effects_chain_config = _json.loads(profile_obj.effects_chain)
+            except Exception:
+                pass
+
+    enqueue_generation(
+        generation_id,
+        run_generation(
+            generation_id=generation_id,
+            profile_id=data.profile_id,
+            text=data.text,
+            language=data.language,
+            engine=engine,
+            model_size=model_size,
+            seed=data.seed,
+            normalize=data.normalize,
+            effects_chain=effects_chain_config,
+            instruct=data.instruct,
+            mode="generate",
+            max_chunk_chars=data.max_chunk_chars,
+            crossfade_ms=data.crossfade_ms,
+        )
+    )
+
+    return generation
+
+
+@router.post("/generate/{generation_id}/retry", response_model=models.GenerationResponse)
+async def retry_generation(generation_id: str, db: Session = Depends(get_db)):
+    """Retry a failed generation using the same parameters."""
+    gen = db.query(DBGeneration).filter_by(id=generation_id).first()
+    if not gen:
+        raise HTTPException(status_code=404, detail="Generation not found")
+
+    if (gen.status or "completed") != "failed":
+        raise HTTPException(status_code=400, detail="Only failed generations can be retried")
+
+    gen.status = "generating"
+    gen.error = None
+    gen.audio_path = ""
+    gen.duration = 0
+    db.commit()
+    db.refresh(gen)
+
+    task_manager = get_task_manager()
+    task_manager.start_generation(
+        task_id=generation_id,
+        profile_id=gen.profile_id,
+        text=gen.text,
+    )
+
+    enqueue_generation(
+        generation_id,
+        run_generation(
+            generation_id=generation_id,
+            profile_id=gen.profile_id,
+            text=gen.text,
+            language=gen.language,
+            engine=gen.engine or "qwen",
+            model_size=gen.model_size or "1.7B",
+            seed=gen.seed,
+            instruct=gen.instruct,
+            mode="retry",
+        )
+    )
+
+    return models.GenerationResponse.model_validate(gen)
+
+
+@router.post(
+    "/generate/{generation_id}/regenerate",
+    response_model=models.GenerationResponse,
+)
+async def regenerate_generation(generation_id: str, db: Session = Depends(get_db)):
+    """Re-run TTS with the same parameters and save the result as a new version."""
+    gen = db.query(DBGeneration).filter_by(id=generation_id).first()
+    if not gen:
+        raise HTTPException(status_code=404, detail="Generation not found")
+    if (gen.status or "completed") != "completed":
+        raise HTTPException(status_code=400, detail="Generation must be completed to regenerate")
+
+    gen.status = "generating"
+    gen.error = None
+    db.commit()
+    db.refresh(gen)
+
+    task_manager = get_task_manager()
+    task_manager.start_generation(
+        task_id=generation_id,
+        profile_id=gen.profile_id,
+        text=gen.text,
+    )
+
+    version_id = str(uuid.uuid4())
+
+    enqueue_generation(
+        generation_id,
+        run_generation(
+            generation_id=generation_id,
+            profile_id=gen.profile_id,
+            text=gen.text,
+            language=gen.language,
+            engine=gen.engine or "qwen",
+            model_size=gen.model_size or "1.7B",
+            seed=gen.seed,
+            instruct=gen.instruct,
+            mode="regenerate",
+            version_id=version_id,
+        )
+    )
+
+    return models.GenerationResponse.model_validate(gen)
+
+
+@router.post("/generate/{generation_id}/cancel")
+async def cancel_generation(generation_id: str, db: Session = Depends(get_db)):
+    """Cancel a queued or running generation."""
+    gen = db.query(DBGeneration).filter_by(id=generation_id).first()
+    if not gen:
+        raise HTTPException(status_code=404, detail="Generation not found")
+
+    if (gen.status or "completed") not in ("loading_model", "generating"):
+        raise HTTPException(status_code=400, detail="Only active generations can be cancelled")
+
+    cancellation_state = cancel_generation_job(generation_id)
+    if cancellation_state is None:
+        raise HTTPException(status_code=409, detail="Generation is no longer cancellable")
+
+    if cancellation_state == "queued":
+        task_manager = get_task_manager()
+        task_manager.complete_generation(generation_id)
+        await history.update_generation_status(
+            generation_id=generation_id,
+            status="failed",
+            db=db,
+            error="Generation cancelled",
+        )
+        return {"message": "Queued generation cancelled"}
+
+    return {"message": "Generation cancellation requested"}
+
+
+@router.get("/generate/{generation_id}/status")
+async def get_generation_status(generation_id: str, db: Session = Depends(get_db)):
+    """SSE endpoint that streams generation status updates."""
+    import json
+
+    async def event_stream():
+        try:
+            while True:
+                db.expire_all()
+                gen = db.query(DBGeneration).filter_by(id=generation_id).first()
+                if not gen:
+                    yield f"data: {json.dumps({'status': 'not_found', 'id': generation_id})}\n\n"
+                    return
+
+                payload = {
+                    "id": gen.id,
+                    "status": gen.status or "completed",
+                    "duration": gen.duration,
+                    "error": gen.error,
+                }
+                yield f"data: {json.dumps(payload)}\n\n"
+
+                if (gen.status or "completed") in ("completed", "failed"):
+                    return
+
+                await asyncio.sleep(1)
+        except (BrokenPipeError, ConnectionResetError, asyncio.CancelledError):
+            logger.debug("SSE client disconnected for generation %s", generation_id)
+
+    return StreamingResponse(
+        event_stream(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "X-Accel-Buffering": "no",
+        },
+    )
+
+
+@router.post("/generate/stream")
+async def stream_speech(
+    data: models.GenerationRequest,
+    db: Session = Depends(get_db),
+):
+    """Generate speech and stream the WAV audio directly without saving to disk."""
+    from ..backends import get_tts_backend_for_engine, ensure_model_cached_or_raise, load_engine_model, engine_needs_trim
+
+    profile = await profiles.get_profile(data.profile_id, db)
+    if not profile:
+        raise HTTPException(status_code=404, detail="Profile not found")
+
+    engine = _resolve_generation_engine(data, profile)
+    try:
+        profiles.validate_profile_engine(profile, engine)
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+    tts_model = get_tts_backend_for_engine(engine)
+    model_size = data.model_size or "1.7B"
+
+    await ensure_model_cached_or_raise(engine, model_size)
+    await load_engine_model(engine, model_size)
+
+    voice_prompt = await profiles.create_voice_prompt_for_profile(
+        data.profile_id,
+        db,
+        engine=engine,
+    )
+
+    from ..utils.chunked_tts import generate_chunked
+
+    trim_fn = None
+    if engine_needs_trim(engine):
+        from ..utils.audio import trim_tts_output
+
+        trim_fn = trim_tts_output
+
+    audio, sample_rate = await generate_chunked(
+        tts_model,
+        data.text,
+        voice_prompt,
+        language=data.language,
+        seed=data.seed,
+        instruct=data.instruct,
+        max_chunk_chars=data.max_chunk_chars,
+        crossfade_ms=data.crossfade_ms,
+        trim_fn=trim_fn,
+    )
+
+    effects_chain_config = None
+    if data.effects_chain is not None:
+        effects_chain_config = [e.model_dump() for e in data.effects_chain]
+    elif profile.effects_chain:
+        import json as _json
+
+        try:
+            effects_chain_config = _json.loads(profile.effects_chain)
+        except Exception:
+            effects_chain_config = None
+
+    if effects_chain_config:
+        from ..utils.effects import apply_effects
+
+        audio = apply_effects(audio, sample_rate, effects_chain_config)
+
+    if data.normalize:
+        from ..utils.audio import normalize_audio
+
+        audio = normalize_audio(audio)
+
+    wav_bytes = tts.audio_to_wav_bytes(audio, sample_rate)
+
+    async def _wav_stream():
+        try:
+            chunk_size = 64 * 1024
+            for i in range(0, len(wav_bytes), chunk_size):
+                yield wav_bytes[i : i + chunk_size]
+        except (BrokenPipeError, ConnectionResetError, asyncio.CancelledError):
+            logger.debug("Client disconnected during audio stream")
+
+    return StreamingResponse(
+        _wav_stream(),
+        media_type="audio/wav",
+        headers={"Content-Disposition": 'attachment; filename="speech.wav"'},
+    )

backend/routes/health.py

@@ -0,0 +1,248 @@
+"""Health and infrastructure endpoints."""
+
+import asyncio
+import os
+import signal
+from pathlib import Path
+
+import torch
+from fastapi import APIRouter, Depends
+from fastapi.responses import FileResponse
+from sqlalchemy.orm import Session
+
+from .. import config, models
+from ..services import tts
+from ..database import get_db
+from ..utils.platform_detect import get_backend_type
+
+router = APIRouter()
+
+# Frontend build directory — present in Docker, absent in dev/API-only mode
+_frontend_dir = Path(__file__).resolve().parent.parent.parent / "frontend"
+
+
+@router.get("/")
+async def root():
+    """Root endpoint — serves SPA index.html in Docker, JSON otherwise."""
+    from .. import __version__
+
+    index = _frontend_dir / "index.html"
+    if index.is_file():
+        return FileResponse(index, media_type="text/html")
+    return {"message": "voicebox API", "version": __version__}
+
+
+@router.post("/shutdown")
+async def shutdown():
+    """Gracefully shutdown the server."""
+
+    async def shutdown_async():
+        await asyncio.sleep(0.1)
+        os.kill(os.getpid(), signal.SIGTERM)
+
+    asyncio.create_task(shutdown_async())
+    return {"message": "Shutting down..."}
+
+
+@router.post("/watchdog/disable")
+async def watchdog_disable():
+    """Disable the parent process watchdog so the server keeps running."""
+    from backend.server import disable_watchdog
+
+    disable_watchdog()
+    return {"message": "Watchdog disabled"}
+
+
+@router.get("/health", response_model=models.HealthResponse)
+async def health():
+    """Health check endpoint."""
+    from huggingface_hub import constants as hf_constants
+    from pathlib import Path
+
+    tts_model = tts.get_tts_model()
+    backend_type = get_backend_type()
+
+    has_cuda = torch.cuda.is_available()
+    has_mps = hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
+
+    has_xpu = False
+    xpu_name = None
+    try:
+        import intel_extension_for_pytorch as ipex  # noqa: F401 -- side-effect import enables XPU
+
+        if hasattr(torch, "xpu") and torch.xpu.is_available():
+            has_xpu = True
+            try:
+                xpu_name = torch.xpu.get_device_name(0)
+            except Exception:
+                xpu_name = "Intel GPU"
+    except ImportError:
+        pass
+
+    has_directml = False
+    directml_name = None
+    try:
+        import torch_directml
+
+        if torch_directml.device_count() > 0:
+            has_directml = True
+            try:
+                directml_name = torch_directml.device_name(0)
+            except Exception:
+                directml_name = "DirectML GPU"
+    except ImportError:
+        pass
+
+    gpu_compat_warning = None
+    if has_cuda:
+        from ..backends.base import check_cuda_compatibility
+
+        _compatible, gpu_compat_warning = check_cuda_compatibility()
+
+    gpu_available = has_cuda or has_mps or has_xpu or has_directml or backend_type == "mlx"
+
+    gpu_type = None
+    if has_cuda:
+        gpu_type = f"CUDA ({torch.cuda.get_device_name(0)})"
+    elif has_mps:
+        gpu_type = "MPS (Apple Silicon)"
+    elif backend_type == "mlx":
+        gpu_type = "Metal (Apple Silicon via MLX)"
+    elif has_xpu:
+        gpu_type = f"XPU ({xpu_name})"
+    elif has_directml:
+        gpu_type = f"DirectML ({directml_name})"
+
+    vram_used = None
+    if has_cuda:
+        vram_used = torch.cuda.memory_allocated() / 1024 / 1024
+    elif has_xpu:
+        try:
+            vram_used = torch.xpu.memory_allocated() / 1024 / 1024
+        except Exception:
+            pass  # memory_allocated() may not be available on all IPEX versions
+
+    model_loaded = False
+    model_size = None
+    try:
+        if tts_model.is_loaded():
+            model_loaded = True
+            model_size = getattr(tts_model, "_current_model_size", None)
+            if not model_size:
+                model_size = getattr(tts_model, "model_size", None)
+    except Exception:
+        model_loaded = False
+        model_size = None
+
+    model_downloaded = None
+    try:
+        from ..backends import get_model_config
+
+        default_config = get_model_config("qwen-tts-1.7B")
+        default_model_id = default_config.hf_repo_id if default_config else "Qwen/Qwen3-TTS-12Hz-1.7B-Base"
+
+        try:
+            from huggingface_hub import scan_cache_dir
+
+            cache_info = scan_cache_dir()
+            for repo in cache_info.repos:
+                if repo.repo_id == default_model_id:
+                    model_downloaded = True
+                    break
+        except (ImportError, Exception):
+            cache_dir = hf_constants.HF_HUB_CACHE
+            repo_cache = Path(cache_dir) / ("models--" + default_model_id.replace("/", "--"))
+            if repo_cache.exists():
+                has_model_files = (
+                    any(repo_cache.rglob("*.bin"))
+                    or any(repo_cache.rglob("*.safetensors"))
+                    or any(repo_cache.rglob("*.pt"))
+                    or any(repo_cache.rglob("*.pth"))
+                    or any(repo_cache.rglob("*.npz"))
+                )
+                model_downloaded = has_model_files
+    except Exception:
+        pass
+
+    return models.HealthResponse(
+        status="healthy",
+        model_loaded=model_loaded,
+        model_downloaded=model_downloaded,
+        model_size=model_size,
+        gpu_available=gpu_available,
+        gpu_type=gpu_type,
+        vram_used_mb=vram_used,
+        backend_type=backend_type,
+        backend_variant=os.environ.get(
+            "VOICEBOX_BACKEND_VARIANT",
+            "cuda" if torch.cuda.is_available() else ("xpu" if has_xpu else "cpu"),
+        ),
+        gpu_compatibility_warning=gpu_compat_warning,
+    )
+
+
+@router.get("/health/filesystem", response_model=models.FilesystemHealthResponse)
+async def filesystem_health():
+    """Check filesystem health: directory existence, write permissions, and disk space."""
+    import shutil
+
+    dirs_to_check = {
+        "generations": config.get_generations_dir(),
+        "profiles": config.get_profiles_dir(),
+        "data": config.get_data_dir(),
+    }
+
+    checks: list[models.DirectoryCheck] = []
+    all_ok = True
+
+    for _label, dir_path in dirs_to_check.items():
+        exists = dir_path.exists()
+        writable = False
+        error = None
+        if exists:
+            probe = dir_path / ".voicebox_probe"
+            try:
+                probe.write_text("ok")
+                probe.unlink()
+                writable = True
+            except PermissionError:
+                error = "Permission denied"
+            except OSError as e:
+                error = str(e)
+            finally:
+                try:
+                    probe.unlink(missing_ok=True)
+                except Exception:
+                    pass
+        else:
+            error = "Directory does not exist"
+
+        if not exists or not writable:
+            all_ok = False
+
+        checks.append(
+            models.DirectoryCheck(
+                path=str(dir_path.resolve()),
+                exists=exists,
+                writable=writable,
+                error=error,
+            )
+        )
+
+    disk_free_mb = None
+    disk_total_mb = None
+    try:
+        usage = shutil.disk_usage(str(config.get_data_dir()))
+        disk_free_mb = round(usage.free / (1024 * 1024), 1)
+        disk_total_mb = round(usage.total / (1024 * 1024), 1)
+        if disk_free_mb < 500:
+            all_ok = False
+    except OSError:
+        all_ok = False
+
+    return models.FilesystemHealthResponse(
+        healthy=all_ok,
+        disk_free_mb=disk_free_mb,
+        disk_total_mb=disk_total_mb,
+        directories=checks,
+    )

backend/routes/history.py

@@ -0,0 +1,189 @@
+"""Generation history endpoints."""
+
+import io
+
+from fastapi import APIRouter, Depends, File, HTTPException, UploadFile
+from fastapi.responses import FileResponse, StreamingResponse
+from sqlalchemy.orm import Session
+
+from .. import config, models
+from ..services import export_import, history
+from ..app import safe_content_disposition
+from ..database import Generation as DBGeneration, VoiceProfile as DBVoiceProfile, get_db
+
+router = APIRouter()
+
+
+@router.get("/history", response_model=models.HistoryListResponse)
+async def list_history(
+    profile_id: str | None = None,
+    search: str | None = None,
+    limit: int = 50,
+    offset: int = 0,
+    db: Session = Depends(get_db),
+):
+    """List generation history with optional filters."""
+    query = models.HistoryQuery(
+        profile_id=profile_id,
+        search=search,
+        limit=limit,
+        offset=offset,
+    )
+    return await history.list_generations(query, db)
+
+
+@router.get("/history/stats")
+async def get_stats(db: Session = Depends(get_db)):
+    """Get generation statistics."""
+    return await history.get_generation_stats(db)
+
+
+@router.post("/history/import")
+async def import_generation(
+    file: UploadFile = File(...),
+    db: Session = Depends(get_db),
+):
+    """Import a generation from a ZIP archive."""
+    MAX_FILE_SIZE = 50 * 1024 * 1024
+
+    content = await file.read()
+
+    if len(content) > MAX_FILE_SIZE:
+        raise HTTPException(
+            status_code=400, detail=f"File too large. Maximum size is {MAX_FILE_SIZE / (1024 * 1024)}MB"
+        )
+
+    try:
+        result = await export_import.import_generation_from_zip(content, db)
+        return result
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.delete("/history/failed")
+async def clear_failed_generations(db: Session = Depends(get_db)):
+    """Delete every generation with status='failed'. Used by the UI's 'Clear failed' button (#410)."""
+    count = await history.delete_failed_generations(db)
+    return {"deleted": count}
+
+
+@router.get("/history/{generation_id}", response_model=models.HistoryResponse)
+async def get_generation(
+    generation_id: str,
+    db: Session = Depends(get_db),
+):
+    """Get a generation by ID."""
+    result = (
+        db.query(DBGeneration, DBVoiceProfile.name.label("profile_name"))
+        .join(DBVoiceProfile, DBGeneration.profile_id == DBVoiceProfile.id)
+        .filter(DBGeneration.id == generation_id)
+        .first()
+    )
+
+    if not result:
+        raise HTTPException(status_code=404, detail="Generation not found")
+
+    gen, profile_name = result
+    return models.HistoryResponse(
+        id=gen.id,
+        profile_id=gen.profile_id,
+        profile_name=profile_name,
+        text=gen.text,
+        language=gen.language,
+        audio_path=gen.audio_path,
+        duration=gen.duration,
+        seed=gen.seed,
+        instruct=gen.instruct,
+        engine=gen.engine or "qwen",
+        model_size=gen.model_size,
+        status=gen.status or "completed",
+        error=gen.error,
+        is_favorited=bool(gen.is_favorited),
+        created_at=gen.created_at,
+    )
+
+
+@router.post("/history/{generation_id}/favorite")
+async def toggle_favorite(
+    generation_id: str,
+    db: Session = Depends(get_db),
+):
+    """Toggle the favorite status of a generation."""
+    gen = db.query(DBGeneration).filter_by(id=generation_id).first()
+    if not gen:
+        raise HTTPException(status_code=404, detail="Generation not found")
+    gen.is_favorited = not gen.is_favorited
+    db.commit()
+    return {"is_favorited": gen.is_favorited}
+
+
+@router.delete("/history/{generation_id}")
+async def delete_generation(
+    generation_id: str,
+    db: Session = Depends(get_db),
+):
+    """Delete a generation."""
+    success = await history.delete_generation(generation_id, db)
+    if not success:
+        raise HTTPException(status_code=404, detail="Generation not found")
+    return {"message": "Generation deleted successfully"}
+
+
+@router.get("/history/{generation_id}/export")
+async def export_generation(
+    generation_id: str,
+    db: Session = Depends(get_db),
+):
+    """Export a generation as a ZIP archive."""
+    generation = db.query(DBGeneration).filter_by(id=generation_id).first()
+    if not generation:
+        raise HTTPException(status_code=404, detail="Generation not found")
+
+    try:
+        zip_bytes = export_import.export_generation_to_zip(generation_id, db)
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+    safe_text = "".join(c for c in generation.text[:30] if c.isalnum() or c in (" ", "-", "_")).strip()
+    if not safe_text:
+        safe_text = "generation"
+    filename = f"generation-{safe_text}.voicebox.zip"
+
+    return StreamingResponse(
+        io.BytesIO(zip_bytes),
+        media_type="application/zip",
+        headers={"Content-Disposition": safe_content_disposition("attachment", filename)},
+    )
+
+
+@router.get("/history/{generation_id}/export-audio")
+async def export_generation_audio(
+    generation_id: str,
+    db: Session = Depends(get_db),
+):
+    """Export only the audio file from a generation."""
+    generation = db.query(DBGeneration).filter_by(id=generation_id).first()
+    if not generation:
+        raise HTTPException(status_code=404, detail="Generation not found")
+
+    if not generation.audio_path:
+        raise HTTPException(status_code=404, detail="Generation has no audio file")
+
+    audio_path = config.resolve_storage_path(generation.audio_path)
+    if audio_path is None or not audio_path.is_file():
+        raise HTTPException(status_code=404, detail="Audio file not found")
+
+    safe_text = "".join(c for c in generation.text[:30] if c.isalnum() or c in (" ", "-", "_")).strip()
+    if not safe_text:
+        safe_text = "generation"
+    filename = f"{safe_text}.wav"
+
+    return FileResponse(
+        audio_path,
+        media_type="audio/wav",
+        headers={"Content-Disposition": safe_content_disposition("attachment", filename)},
+    )

backend/routes/models.py

@@ -0,0 +1,475 @@
+"""Model management endpoints."""
+
+import asyncio
+import shutil
+from pathlib import Path
+
+from fastapi import APIRouter, Depends, HTTPException
+from fastapi.responses import StreamingResponse
+from sqlalchemy.orm import Session
+
+from .. import models
+from ..utils.platform_detect import get_backend_type
+from ..services.task_queue import create_background_task
+from ..utils.progress import get_progress_manager
+from ..utils.tasks import get_task_manager
+
+router = APIRouter()
+
+
+def _get_dir_size(path: Path) -> int:
+    """Get total size of a directory in bytes."""
+    total = 0
+    for f in path.rglob("*"):
+        if f.is_file():
+            total += f.stat().st_size
+    return total
+
+
+def _copy_with_progress(src: Path, dst: Path, progress_manager, copied_so_far: int, total_bytes: int) -> int:
+    """Copy a directory tree with byte-level progress tracking."""
+    dst.mkdir(parents=True, exist_ok=True)
+    for item in src.iterdir():
+        dest_item = dst / item.name
+        if item.is_dir():
+            copied_so_far = _copy_with_progress(item, dest_item, progress_manager, copied_so_far, total_bytes)
+        else:
+            size = item.stat().st_size
+            shutil.copy2(str(item), str(dest_item))
+            copied_so_far += size
+            progress_manager.update_progress(
+                "migration",
+                copied_so_far,
+                total_bytes,
+                filename=item.name,
+                status="downloading",
+            )
+    return copied_so_far
+
+
+@router.post("/models/load")
+async def load_model(model_size: str = "1.7B"):
+    """Manually load TTS model."""
+    from ..services import tts
+
+    try:
+        tts_model = tts.get_tts_model()
+        await tts_model.load_model_async(model_size)
+        return {"message": f"Model {model_size} loaded successfully"}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.post("/models/unload")
+async def unload_model():
+    """Unload the default Qwen TTS model to free memory."""
+    from ..services import tts
+
+    try:
+        tts.unload_tts_model()
+        return {"message": "Model unloaded successfully"}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.post("/models/{model_name}/unload")
+async def unload_model_by_name(model_name: str):
+    """Unload a specific model from memory without deleting it from disk."""
+    from ..backends import get_model_config, unload_model_by_config
+
+    config = get_model_config(model_name)
+    if not config:
+        raise HTTPException(status_code=400, detail=f"Unknown model: {model_name}")
+
+    try:
+        was_loaded = unload_model_by_config(config)
+        if not was_loaded:
+            return {"message": f"Model {model_name} is not loaded"}
+        return {"message": f"Model {model_name} unloaded successfully"}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e)) from e
+
+
+@router.get("/models/progress/{model_name}")
+async def get_model_progress(model_name: str):
+    """Get model download progress via Server-Sent Events."""
+    progress_manager = get_progress_manager()
+
+    async def event_generator():
+        async for event in progress_manager.subscribe(model_name):
+            yield event
+
+    return StreamingResponse(
+        event_generator(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "X-Accel-Buffering": "no",
+        },
+    )
+
+
+@router.get("/models/cache-dir")
+async def get_models_cache_dir():
+    """Get the path to the HuggingFace model cache directory."""
+    from huggingface_hub import constants as hf_constants
+
+    return {"path": str(Path(hf_constants.HF_HUB_CACHE))}
+
+
+@router.post("/models/migrate")
+async def migrate_models(request: models.ModelMigrateRequest):
+    """Move all downloaded models to a new directory with byte-level progress via SSE."""
+    from huggingface_hub import constants as hf_constants
+
+    source = Path(hf_constants.HF_HUB_CACHE)
+    destination = Path(request.destination)
+
+    if not source.exists():
+        raise HTTPException(status_code=404, detail="Current model cache directory not found")
+
+    if source.resolve() == destination.resolve():
+        raise HTTPException(status_code=400, detail="Source and destination are the same directory")
+
+    if destination.resolve().is_relative_to(source.resolve()):
+        raise HTTPException(status_code=400, detail="Destination cannot be inside the current cache directory")
+
+    progress_manager = get_progress_manager()
+    model_dirs = [d for d in source.iterdir() if d.name.startswith("models--") and d.is_dir()]
+    if not model_dirs:
+        progress_manager.update_progress("migration", 1, 1, status="complete")
+        progress_manager.mark_complete("migration")
+        return {"moved": 0, "errors": [], "source": str(source), "destination": str(destination)}
+
+    destination.mkdir(parents=True, exist_ok=True)
+
+    same_fs = False
+    try:
+        same_fs = source.stat().st_dev == destination.stat().st_dev
+    except OSError:
+        pass
+
+    async def migrate_background():
+        moved = 0
+        errors = []
+        try:
+            if same_fs:
+                total = len(model_dirs)
+                for i, item in enumerate(model_dirs):
+                    dest_item = destination / item.name
+                    try:
+                        if dest_item.exists():
+                            shutil.rmtree(dest_item)
+                        shutil.move(str(item), str(dest_item))
+                        moved += 1
+                        progress_manager.update_progress(
+                            "migration",
+                            i + 1,
+                            total,
+                            filename=item.name,
+                            status="downloading",
+                        )
+                    except Exception as e:
+                        errors.append(f"{item.name}: {str(e)}")
+            else:
+                total_bytes = sum(_get_dir_size(d) for d in model_dirs)
+                progress_manager.update_progress(
+                    "migration", 0, total_bytes, filename="Calculating...", status="downloading"
+                )
+
+                copied = 0
+                for item in model_dirs:
+                    dest_item = destination / item.name
+                    try:
+                        if dest_item.exists():
+                            shutil.rmtree(dest_item)
+                        copied = await asyncio.to_thread(
+                            _copy_with_progress, item, dest_item, progress_manager, copied, total_bytes
+                        )
+                        await asyncio.to_thread(shutil.rmtree, str(item))
+                        moved += 1
+                    except Exception as e:
+                        errors.append(f"{item.name}: {str(e)}")
+
+            progress_manager.update_progress("migration", 1, 1, status="complete")
+            progress_manager.mark_complete("migration")
+        except Exception as e:
+            progress_manager.update_progress("migration", 0, 0, status="error")
+            progress_manager.mark_error("migration", str(e))
+
+    create_background_task(migrate_background())
+
+    return {"source": str(source), "destination": str(destination)}
+
+
+@router.get("/models/migrate/progress")
+async def get_migration_progress():
+    """Get model migration progress via Server-Sent Events."""
+    progress_manager = get_progress_manager()
+
+    async def event_generator():
+        async for event in progress_manager.subscribe("migration"):
+            yield event
+
+    return StreamingResponse(
+        event_generator(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "X-Accel-Buffering": "no",
+        },
+    )
+
+
+@router.get("/models/status", response_model=models.ModelStatusListResponse)
+async def get_model_status():
+    """Get status of all available models."""
+    from huggingface_hub import constants as hf_constants
+
+    backend_type = get_backend_type()
+    task_manager = get_task_manager()
+
+    active_download_names = {task.model_name for task in task_manager.get_active_downloads()}
+
+    try:
+        from huggingface_hub import scan_cache_dir
+
+        use_scan_cache = True
+    except ImportError:
+        use_scan_cache = False
+
+    from ..backends import get_all_model_configs, check_model_loaded
+
+    registry_configs = get_all_model_configs()
+    model_configs = [
+        {
+            "model_name": cfg.model_name,
+            "display_name": cfg.display_name,
+            "hf_repo_id": cfg.hf_repo_id,
+            "model_size": cfg.model_size,
+            "check_loaded": lambda c=cfg: check_model_loaded(c),
+        }
+        for cfg in registry_configs
+    ]
+
+    model_to_repo = {cfg["model_name"]: cfg["hf_repo_id"] for cfg in model_configs}
+    active_download_repos = {model_to_repo.get(name) for name in active_download_names if name in model_to_repo}
+
+    cache_info = None
+    if use_scan_cache:
+        try:
+            cache_info = scan_cache_dir()
+        except Exception:
+            pass
+
+    statuses = []
+
+    for config in model_configs:
+        try:
+            downloaded = False
+            size_mb = None
+            loaded = False
+
+            if cache_info:
+                repo_id = config["hf_repo_id"]
+                for repo in cache_info.repos:
+                    if repo.repo_id == repo_id:
+                        has_model_weights = False
+                        for rev in repo.revisions:
+                            for f in rev.files:
+                                fname = f.file_name.lower()
+                                if fname.endswith((".safetensors", ".bin", ".pt", ".pth", ".npz")):
+                                    has_model_weights = True
+                                    break
+                            if has_model_weights:
+                                break
+
+                        has_incomplete = False
+                        try:
+                            cache_dir = hf_constants.HF_HUB_CACHE
+                            blobs_dir = Path(cache_dir) / ("models--" + repo_id.replace("/", "--")) / "blobs"
+                            if blobs_dir.exists():
+                                has_incomplete = any(blobs_dir.glob("*.incomplete"))
+                        except Exception:
+                            pass
+
+                        if has_model_weights and not has_incomplete:
+                            downloaded = True
+                            try:
+                                total_size = sum(revision.size_on_disk for revision in repo.revisions)
+                                size_mb = total_size / (1024 * 1024)
+                            except Exception:
+                                pass
+                        break
+
+            if not downloaded:
+                try:
+                    cache_dir = hf_constants.HF_HUB_CACHE
+                    repo_cache = Path(cache_dir) / ("models--" + config["hf_repo_id"].replace("/", "--"))
+
+                    if repo_cache.exists():
+                        blobs_dir = repo_cache / "blobs"
+                        has_incomplete = blobs_dir.exists() and any(blobs_dir.glob("*.incomplete"))
+
+                        if not has_incomplete:
+                            snapshots_dir = repo_cache / "snapshots"
+                            has_model_files = False
+                            if snapshots_dir.exists():
+                                has_model_files = (
+                                    any(snapshots_dir.rglob("*.bin"))
+                                    or any(snapshots_dir.rglob("*.safetensors"))
+                                    or any(snapshots_dir.rglob("*.pt"))
+                                    or any(snapshots_dir.rglob("*.pth"))
+                                    or any(snapshots_dir.rglob("*.npz"))
+                                )
+
+                            if has_model_files:
+                                downloaded = True
+                                try:
+                                    total_size = sum(
+                                        f.stat().st_size
+                                        for f in repo_cache.rglob("*")
+                                        if f.is_file() and not f.name.endswith(".incomplete")
+                                    )
+                                    size_mb = total_size / (1024 * 1024)
+                                except Exception:
+                                    pass
+                except Exception:
+                    pass
+
+            try:
+                loaded = config["check_loaded"]()
+            except Exception:
+                loaded = False
+
+            is_downloading = config["hf_repo_id"] in active_download_repos
+
+            if is_downloading:
+                downloaded = False
+                size_mb = None
+
+            statuses.append(
+                models.ModelStatus(
+                    model_name=config["model_name"],
+                    display_name=config["display_name"],
+                    hf_repo_id=config["hf_repo_id"],
+                    downloaded=downloaded,
+                    downloading=is_downloading,
+                    size_mb=size_mb,
+                    loaded=loaded,
+                )
+            )
+        except Exception:
+            try:
+                loaded = config["check_loaded"]()
+            except Exception:
+                loaded = False
+
+            is_downloading = config["hf_repo_id"] in active_download_repos
+
+            statuses.append(
+                models.ModelStatus(
+                    model_name=config["model_name"],
+                    display_name=config["display_name"],
+                    hf_repo_id=config["hf_repo_id"],
+                    downloaded=False,
+                    downloading=is_downloading,
+                    size_mb=None,
+                    loaded=loaded,
+                )
+            )
+
+    return models.ModelStatusListResponse(models=statuses)
+
+
+@router.post("/models/download")
+async def trigger_model_download(request: models.ModelDownloadRequest):
+    """Trigger download of a specific model."""
+    from ..backends import get_model_config, get_model_load_func
+
+    task_manager = get_task_manager()
+    progress_manager = get_progress_manager()
+
+    config = get_model_config(request.model_name)
+    if not config:
+        raise HTTPException(status_code=400, detail=f"Unknown model: {request.model_name}")
+
+    load_func = get_model_load_func(config)
+
+    async def download_in_background():
+        try:
+            result = load_func()
+            if asyncio.iscoroutine(result):
+                await result
+            task_manager.complete_download(request.model_name)
+        except Exception as e:
+            task_manager.error_download(request.model_name, str(e))
+
+    task_manager.start_download(request.model_name)
+
+    progress_manager.update_progress(
+        model_name=request.model_name,
+        current=0,
+        total=0,
+        filename="Connecting to HuggingFace...",
+        status="downloading",
+    )
+
+    create_background_task(download_in_background())
+
+    return {"message": f"Model {request.model_name} download started"}
+
+
+@router.post("/models/download/cancel")
+async def cancel_model_download(request: models.ModelDownloadRequest):
+    """Cancel or dismiss an errored/stale download task."""
+    task_manager = get_task_manager()
+    progress_manager = get_progress_manager()
+
+    removed = task_manager.cancel_download(request.model_name)
+
+    progress_removed = False
+    with progress_manager._lock:
+        if request.model_name in progress_manager._progress:
+            del progress_manager._progress[request.model_name]
+            progress_removed = True
+
+    if removed or progress_removed:
+        return {"message": f"Download task for {request.model_name} cancelled"}
+    return {"message": f"No active task found for {request.model_name}"}
+
+
+@router.delete("/models/{model_name}")
+async def delete_model(model_name: str):
+    """Delete a downloaded model from the HuggingFace cache."""
+    from huggingface_hub import constants as hf_constants
+    from ..backends import get_model_config, unload_model_by_config
+
+    config = get_model_config(model_name)
+    if not config:
+        raise HTTPException(status_code=400, detail=f"Unknown model: {model_name}")
+
+    hf_repo_id = config.hf_repo_id
+
+    try:
+        unload_model_by_config(config)
+
+        cache_dir = hf_constants.HF_HUB_CACHE
+        repo_cache_dir = Path(cache_dir) / ("models--" + hf_repo_id.replace("/", "--"))
+
+        if not repo_cache_dir.exists():
+            raise HTTPException(status_code=404, detail=f"Model {model_name} not found in cache")
+
+        try:
+            shutil.rmtree(repo_cache_dir)
+        except OSError as e:
+            raise HTTPException(status_code=500, detail=f"Failed to delete model cache directory: {str(e)}")
+
+        return {"message": f"Model {model_name} deleted successfully"}
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Failed to delete model: {str(e)}")

backend/routes/profiles.py

@@ -0,0 +1,363 @@
+"""Voice profile endpoints."""
+
+import io
+import json as _json
+import logging
+import tempfile
+from datetime import datetime
+from pathlib import Path
+
+from fastapi import APIRouter, Depends, File, Form, HTTPException, UploadFile
+from fastapi.responses import FileResponse, StreamingResponse
+from sqlalchemy.orm import Session
+
+from .. import config, models
+from ..app import safe_content_disposition
+from ..database import VoiceProfile as DBVoiceProfile, get_db
+from ..services import channels, export_import, profiles
+from ..services.profiles import _profile_to_response
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter()
+
+
+@router.post("/profiles", response_model=models.VoiceProfileResponse)
+async def create_profile(
+    data: models.VoiceProfileCreate,
+    db: Session = Depends(get_db),
+):
+    """Create a new voice profile."""
+    try:
+        return await profiles.create_profile(data, db)
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.get("/profiles", response_model=list[models.VoiceProfileResponse])
+async def list_profiles(db: Session = Depends(get_db)):
+    """List all voice profiles."""
+    return await profiles.list_profiles(db)
+
+
+@router.post("/profiles/import", response_model=models.VoiceProfileResponse)
+async def import_profile(
+    file: UploadFile = File(...),
+    db: Session = Depends(get_db),
+):
+    """Import a voice profile from a ZIP archive."""
+    MAX_FILE_SIZE = 100 * 1024 * 1024
+
+    content = await file.read()
+
+    if len(content) > MAX_FILE_SIZE:
+        raise HTTPException(
+            status_code=400, detail=f"File too large. Maximum size is {MAX_FILE_SIZE / (1024 * 1024)}MB"
+        )
+
+    try:
+        profile = await export_import.import_profile_from_zip(content, db)
+        return profile
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+# ── Preset Voice Endpoints ───────────────────────────────────────────
+# These MUST be declared before /profiles/{profile_id} to avoid the
+# wildcard swallowing "presets" as a profile_id.
+
+
+@router.get("/profiles/presets/{engine}")
+async def list_preset_voices(engine: str):
+    """List available preset voices for an engine."""
+    if engine == "kokoro":
+        from ..backends.kokoro_backend import KOKORO_VOICES
+
+        return {
+            "engine": engine,
+            "voices": [
+                {
+                    "voice_id": vid,
+                    "name": name,
+                    "gender": gender,
+                    "language": lang,
+                }
+                for vid, name, gender, lang in KOKORO_VOICES
+            ],
+        }
+    if engine == "qwen_custom_voice":
+        from ..backends.qwen_custom_voice_backend import QWEN_CUSTOM_VOICES
+
+        return {
+            "engine": engine,
+            "voices": [
+                {
+                    "voice_id": speaker_id,
+                    "name": display_name,
+                    "gender": gender,
+                    "language": lang,
+                }
+                for speaker_id, display_name, gender, lang, _desc in QWEN_CUSTOM_VOICES
+            ],
+        }
+    return {"engine": engine, "voices": []}
+
+@router.get("/profiles/{profile_id}", response_model=models.VoiceProfileResponse)
+async def get_profile(
+    profile_id: str,
+    db: Session = Depends(get_db),
+):
+    """Get a voice profile by ID."""
+    profile = await profiles.get_profile(profile_id, db)
+    if not profile:
+        raise HTTPException(status_code=404, detail="Profile not found")
+    return profile
+
+
+@router.put("/profiles/{profile_id}", response_model=models.VoiceProfileResponse)
+async def update_profile(
+    profile_id: str,
+    data: models.VoiceProfileCreate,
+    db: Session = Depends(get_db),
+):
+    """Update a voice profile."""
+    try:
+        profile = await profiles.update_profile(profile_id, data, db)
+        if not profile:
+            raise HTTPException(status_code=404, detail="Profile not found")
+        return profile
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.delete("/profiles/{profile_id}")
+async def delete_profile(
+    profile_id: str,
+    db: Session = Depends(get_db),
+):
+    """Delete a voice profile."""
+    success = await profiles.delete_profile(profile_id, db)
+    if not success:
+        raise HTTPException(status_code=404, detail="Profile not found")
+    return {"message": "Profile deleted successfully"}
+
+
+SAMPLE_MAX_FILE_SIZE = 50 * 1024 * 1024  # 50 MB
+SAMPLE_UPLOAD_CHUNK_SIZE = 1024 * 1024  # 1 MB
+
+
+@router.post("/profiles/{profile_id}/samples", response_model=models.ProfileSampleResponse)
+async def add_profile_sample(
+    profile_id: str,
+    file: UploadFile = File(...),
+    reference_text: str = Form(...),
+    db: Session = Depends(get_db),
+):
+    """Add a sample to a voice profile."""
+    _allowed_audio_exts = {".wav", ".mp3", ".m4a", ".ogg", ".flac", ".aac", ".webm", ".opus"}
+    _uploaded_ext = Path(file.filename or "").suffix.lower()
+    file_suffix = _uploaded_ext if _uploaded_ext in _allowed_audio_exts else ".wav"
+
+    with tempfile.NamedTemporaryFile(suffix=file_suffix, delete=False) as tmp:
+        total_size = 0
+        while chunk := await file.read(SAMPLE_UPLOAD_CHUNK_SIZE):
+            total_size += len(chunk)
+            if total_size > SAMPLE_MAX_FILE_SIZE:
+                Path(tmp.name).unlink(missing_ok=True)
+                raise HTTPException(
+                    status_code=413,
+                    detail=f"File too large (max {SAMPLE_MAX_FILE_SIZE // (1024 * 1024)} MB)",
+                )
+            tmp.write(chunk)
+        tmp_path = tmp.name
+
+    try:
+        sample = await profiles.add_profile_sample(
+            profile_id,
+            tmp_path,
+            reference_text,
+            db,
+        )
+        return sample
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Failed to process audio file: {str(e)}")
+    finally:
+        Path(tmp_path).unlink(missing_ok=True)
+
+
+@router.get("/profiles/{profile_id}/samples", response_model=list[models.ProfileSampleResponse])
+async def get_profile_samples(
+    profile_id: str,
+    db: Session = Depends(get_db),
+):
+    """Get all samples for a profile."""
+    return await profiles.get_profile_samples(profile_id, db)
+
+
+@router.delete("/profiles/samples/{sample_id}")
+async def delete_profile_sample(
+    sample_id: str,
+    db: Session = Depends(get_db),
+):
+    """Delete a profile sample."""
+    success = await profiles.delete_profile_sample(sample_id, db)
+    if not success:
+        raise HTTPException(status_code=404, detail="Sample not found")
+    return {"message": "Sample deleted successfully"}
+
+
+@router.put("/profiles/samples/{sample_id}", response_model=models.ProfileSampleResponse)
+async def update_profile_sample(
+    sample_id: str,
+    data: models.ProfileSampleUpdate,
+    db: Session = Depends(get_db),
+):
+    """Update a profile sample's reference text."""
+    sample = await profiles.update_profile_sample(sample_id, data.reference_text, db)
+    if not sample:
+        raise HTTPException(status_code=404, detail="Sample not found")
+    return sample
+
+
+@router.post("/profiles/{profile_id}/avatar", response_model=models.VoiceProfileResponse)
+async def upload_profile_avatar(
+    profile_id: str,
+    file: UploadFile = File(...),
+    db: Session = Depends(get_db),
+):
+    """Upload or update avatar image for a profile."""
+    with tempfile.NamedTemporaryFile(delete=False, suffix=Path(file.filename).suffix) as tmp:
+        content = await file.read()
+        tmp.write(content)
+        tmp_path = tmp.name
+
+    try:
+        profile = await profiles.upload_avatar(profile_id, tmp_path, db)
+        return profile
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+    finally:
+        Path(tmp_path).unlink(missing_ok=True)
+
+
+@router.get("/profiles/{profile_id}/avatar")
+async def get_profile_avatar(
+    profile_id: str,
+    db: Session = Depends(get_db),
+):
+    """Get avatar image for a profile."""
+    profile = await profiles.get_profile(profile_id, db)
+    if not profile:
+        raise HTTPException(status_code=404, detail="Profile not found")
+
+    if not profile.avatar_path:
+        raise HTTPException(status_code=404, detail="No avatar found for this profile")
+
+    avatar_path = config.resolve_storage_path(profile.avatar_path)
+    if avatar_path is None or not avatar_path.exists():
+        raise HTTPException(status_code=404, detail="Avatar file not found")
+
+    return FileResponse(avatar_path)
+
+
+@router.delete("/profiles/{profile_id}/avatar")
+async def delete_profile_avatar(
+    profile_id: str,
+    db: Session = Depends(get_db),
+):
+    """Delete avatar image for a profile."""
+    success = await profiles.delete_avatar(profile_id, db)
+    if not success:
+        raise HTTPException(status_code=404, detail="Profile not found or no avatar to delete")
+    return {"message": "Avatar deleted successfully"}
+
+
+@router.get("/profiles/{profile_id}/export")
+async def export_profile(
+    profile_id: str,
+    db: Session = Depends(get_db),
+):
+    """Export a voice profile as a ZIP archive."""
+    try:
+        profile = await profiles.get_profile(profile_id, db)
+        if not profile:
+            raise HTTPException(status_code=404, detail="Profile not found")
+
+        zip_bytes = export_import.export_profile_to_zip(profile_id, db)
+
+        safe_name = "".join(c for c in profile.name if c.isalnum() or c in (" ", "-", "_")).strip()
+        if not safe_name:
+            safe_name = "profile"
+        filename = f"profile-{safe_name}.voicebox.zip"
+
+        return StreamingResponse(
+            io.BytesIO(zip_bytes),
+            media_type="application/zip",
+            headers={"Content-Disposition": safe_content_disposition("attachment", filename)},
+        )
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/profiles/{profile_id}/channels")
+async def get_profile_channels(
+    profile_id: str,
+    db: Session = Depends(get_db),
+):
+    """Get list of channel IDs assigned to a profile."""
+    try:
+        channel_ids = await channels.get_profile_channels(profile_id, db)
+        return {"channel_ids": channel_ids}
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.put("/profiles/{profile_id}/channels")
+async def set_profile_channels(
+    profile_id: str,
+    data: models.ProfileChannelAssignment,
+    db: Session = Depends(get_db),
+):
+    """Set which channels a profile is assigned to."""
+    try:
+        await channels.set_profile_channels(profile_id, data, db)
+        return {"message": "Profile channels updated successfully"}
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.put("/profiles/{profile_id}/effects", response_model=models.VoiceProfileResponse)
+async def update_profile_effects(
+    profile_id: str,
+    data: models.ProfileEffectsUpdate,
+    db: Session = Depends(get_db),
+):
+    """Set or clear the default effects chain for a voice profile."""
+    profile = db.query(DBVoiceProfile).filter_by(id=profile_id).first()
+    if not profile:
+        raise HTTPException(status_code=404, detail="Profile not found")
+
+    if data.effects_chain is not None:
+        from ..utils.effects import validate_effects_chain
+
+        chain_dicts = [e.model_dump() for e in data.effects_chain]
+        error = validate_effects_chain(chain_dicts)
+        if error:
+            raise HTTPException(status_code=400, detail=error)
+        profile.effects_chain = _json.dumps(chain_dicts)
+    else:
+        profile.effects_chain = None
+
+    profile.updated_at = datetime.utcnow()
+    db.commit()
+    db.refresh(profile)
+
+    return _profile_to_response(profile)

backend/routes/stories.py

@@ -0,0 +1,223 @@
+"""Story endpoints."""
+
+import io
+
+from fastapi import APIRouter, Depends, HTTPException
+from fastapi.responses import StreamingResponse
+from sqlalchemy.orm import Session
+
+from .. import database, models
+from ..services import stories
+from ..app import safe_content_disposition
+from ..database import get_db
+
+router = APIRouter()
+
+
+@router.get("/stories", response_model=list[models.StoryResponse])
+async def list_stories(db: Session = Depends(get_db)):
+    """List all stories."""
+    return await stories.list_stories(db)
+
+
+@router.post("/stories", response_model=models.StoryResponse)
+async def create_story(
+    data: models.StoryCreate,
+    db: Session = Depends(get_db),
+):
+    """Create a new story."""
+    try:
+        return await stories.create_story(data, db)
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.get("/stories/{story_id}", response_model=models.StoryDetailResponse)
+async def get_story(
+    story_id: str,
+    db: Session = Depends(get_db),
+):
+    """Get a story with all its items."""
+    story = await stories.get_story(story_id, db)
+    if not story:
+        raise HTTPException(status_code=404, detail="Story not found")
+    return story
+
+
+@router.put("/stories/{story_id}", response_model=models.StoryResponse)
+async def update_story(
+    story_id: str,
+    data: models.StoryCreate,
+    db: Session = Depends(get_db),
+):
+    """Update a story."""
+    story = await stories.update_story(story_id, data, db)
+    if not story:
+        raise HTTPException(status_code=404, detail="Story not found")
+    return story
+
+
+@router.delete("/stories/{story_id}")
+async def delete_story(
+    story_id: str,
+    db: Session = Depends(get_db),
+):
+    """Delete a story."""
+    success = await stories.delete_story(story_id, db)
+    if not success:
+        raise HTTPException(status_code=404, detail="Story not found")
+    return {"message": "Story deleted successfully"}
+
+
+@router.post("/stories/{story_id}/items", response_model=models.StoryItemDetail)
+async def add_story_item(
+    story_id: str,
+    data: models.StoryItemCreate,
+    db: Session = Depends(get_db),
+):
+    """Add a generation to a story."""
+    item = await stories.add_item_to_story(story_id, data, db)
+    if not item:
+        raise HTTPException(status_code=404, detail="Story or generation not found")
+    return item
+
+
+@router.delete("/stories/{story_id}/items/{item_id}")
+async def remove_story_item(
+    story_id: str,
+    item_id: str,
+    db: Session = Depends(get_db),
+):
+    """Remove a story item from a story."""
+    success = await stories.remove_item_from_story(story_id, item_id, db)
+    if not success:
+        raise HTTPException(status_code=404, detail="Story item not found")
+    return {"message": "Item removed successfully"}
+
+
+@router.put("/stories/{story_id}/items/times")
+async def update_story_item_times(
+    story_id: str,
+    data: models.StoryItemBatchUpdate,
+    db: Session = Depends(get_db),
+):
+    """Update story item timecodes."""
+    success = await stories.update_story_item_times(story_id, data, db)
+    if not success:
+        raise HTTPException(status_code=400, detail="Invalid timecode update request")
+    return {"message": "Item timecodes updated successfully"}
+
+
+@router.put("/stories/{story_id}/items/reorder", response_model=list[models.StoryItemDetail])
+async def reorder_story_items(
+    story_id: str,
+    data: models.StoryItemReorder,
+    db: Session = Depends(get_db),
+):
+    """Reorder story items and recalculate timecodes."""
+    items = await stories.reorder_story_items(story_id, data.generation_ids, db)
+    if items is None:
+        raise HTTPException(
+            status_code=400, detail="Invalid reorder request - ensure all generation IDs belong to this story"
+        )
+    return items
+
+
+@router.put("/stories/{story_id}/items/{item_id}/move", response_model=models.StoryItemDetail)
+async def move_story_item(
+    story_id: str,
+    item_id: str,
+    data: models.StoryItemMove,
+    db: Session = Depends(get_db),
+):
+    """Move a story item (update position and/or track)."""
+    item = await stories.move_story_item(story_id, item_id, data, db)
+    if item is None:
+        raise HTTPException(status_code=404, detail="Story item not found")
+    return item
+
+
+@router.put("/stories/{story_id}/items/{item_id}/trim", response_model=models.StoryItemDetail)
+async def trim_story_item(
+    story_id: str,
+    item_id: str,
+    data: models.StoryItemTrim,
+    db: Session = Depends(get_db),
+):
+    """Trim a story item."""
+    item = await stories.trim_story_item(story_id, item_id, data, db)
+    if item is None:
+        raise HTTPException(status_code=404, detail="Story item not found or invalid trim values")
+    return item
+
+
+@router.post("/stories/{story_id}/items/{item_id}/split", response_model=list[models.StoryItemDetail])
+async def split_story_item(
+    story_id: str,
+    item_id: str,
+    data: models.StoryItemSplit,
+    db: Session = Depends(get_db),
+):
+    """Split a story item at a given time, creating two clips."""
+    items = await stories.split_story_item(story_id, item_id, data, db)
+    if items is None:
+        raise HTTPException(status_code=404, detail="Story item not found or invalid split point")
+    return items
+
+
+@router.post("/stories/{story_id}/items/{item_id}/duplicate", response_model=models.StoryItemDetail)
+async def duplicate_story_item(
+    story_id: str,
+    item_id: str,
+    db: Session = Depends(get_db),
+):
+    """Duplicate a story item."""
+    item = await stories.duplicate_story_item(story_id, item_id, db)
+    if item is None:
+        raise HTTPException(status_code=404, detail="Story item not found")
+    return item
+
+
+@router.put("/stories/{story_id}/items/{item_id}/version", response_model=models.StoryItemDetail)
+async def set_story_item_version(
+    story_id: str,
+    item_id: str,
+    data: models.StoryItemVersionUpdate,
+    db: Session = Depends(get_db),
+):
+    """Pin a story item to a specific generation version."""
+    item = await stories.set_story_item_version(story_id, item_id, data, db)
+    if item is None:
+        raise HTTPException(status_code=404, detail="Story item or version not found")
+    return item
+
+
+@router.get("/stories/{story_id}/export-audio")
+async def export_story_audio(
+    story_id: str,
+    db: Session = Depends(get_db),
+):
+    """Export story as single mixed audio file."""
+    try:
+        story = db.query(database.Story).filter_by(id=story_id).first()
+        if not story:
+            raise HTTPException(status_code=404, detail="Story not found")
+
+        audio_bytes = await stories.export_story_audio(story_id, db)
+        if not audio_bytes:
+            raise HTTPException(status_code=400, detail="Story has no audio items")
+
+        safe_name = "".join(c for c in story.name if c.isalnum() or c in (" ", "-", "_")).strip()
+        if not safe_name:
+            safe_name = "story"
+        filename = f"{safe_name}.wav"
+
+        return StreamingResponse(
+            io.BytesIO(audio_bytes),
+            media_type="audio/wav",
+            headers={"Content-Disposition": safe_content_disposition("attachment", filename)},
+        )
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

backend/routes/tasks.py

@@ -0,0 +1,125 @@
+"""Task and cache management endpoints."""
+
+from datetime import datetime
+
+from fastapi import APIRouter
+
+from .. import models
+from ..utils.cache import clear_voice_prompt_cache
+from ..utils.progress import get_progress_manager
+from ..utils.tasks import get_task_manager
+from fastapi import HTTPException
+
+router = APIRouter()
+
+
+@router.post("/tasks/clear")
+async def clear_all_tasks():
+    """Clear all download tasks and progress state."""
+    task_manager = get_task_manager()
+    progress_manager = get_progress_manager()
+
+    task_manager.clear_all()
+
+    with progress_manager._lock:
+        progress_manager._progress.clear()
+        progress_manager._last_notify_time.clear()
+        progress_manager._last_notify_progress.clear()
+
+    return {"message": "All task state cleared"}
+
+
+@router.post("/cache/clear")
+async def clear_cache():
+    """Clear all voice prompt caches (memory and disk)."""
+    try:
+        deleted_count = clear_voice_prompt_cache()
+        return {
+            "message": "Voice prompt cache cleared successfully",
+            "files_deleted": deleted_count,
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Failed to clear cache: {str(e)}")
+
+
+@router.get("/tasks/active", response_model=models.ActiveTasksResponse)
+async def get_active_tasks():
+    """Return all currently active downloads and generations."""
+    task_manager = get_task_manager()
+    progress_manager = get_progress_manager()
+
+    active_downloads = []
+    task_manager_downloads = task_manager.get_active_downloads()
+    progress_active = progress_manager.get_all_active()
+
+    download_map = {task.model_name: task for task in task_manager_downloads}
+    progress_map = {p["model_name"]: p for p in progress_active}
+
+    all_model_names = set(download_map.keys()) | set(progress_map.keys())
+    for model_name in all_model_names:
+        task = download_map.get(model_name)
+        progress = progress_map.get(model_name)
+
+        if task:
+            error = task.error
+            if not error:
+                with progress_manager._lock:
+                    pm_data = progress_manager._progress.get(model_name)
+                    if pm_data:
+                        error = pm_data.get("error")
+            prog = progress or {}
+            if not prog:
+                with progress_manager._lock:
+                    pm_data = progress_manager._progress.get(model_name)
+                    if pm_data:
+                        prog = pm_data
+            active_downloads.append(
+                models.ActiveDownloadTask(
+                    model_name=model_name,
+                    status=task.status,
+                    started_at=task.started_at,
+                    error=error,
+                    progress=prog.get("progress"),
+                    current=prog.get("current"),
+                    total=prog.get("total"),
+                    filename=prog.get("filename"),
+                )
+            )
+        elif progress:
+            timestamp_str = progress.get("timestamp")
+            if timestamp_str:
+                try:
+                    started_at = datetime.fromisoformat(timestamp_str.replace("Z", "+00:00"))
+                except (ValueError, AttributeError):
+                    started_at = datetime.utcnow()
+            else:
+                started_at = datetime.utcnow()
+
+            active_downloads.append(
+                models.ActiveDownloadTask(
+                    model_name=model_name,
+                    status=progress.get("status", "downloading"),
+                    started_at=started_at,
+                    error=progress.get("error"),
+                    progress=progress.get("progress"),
+                    current=progress.get("current"),
+                    total=progress.get("total"),
+                    filename=progress.get("filename"),
+                )
+            )
+
+    active_generations = []
+    for gen_task in task_manager.get_active_generations():
+        active_generations.append(
+            models.ActiveGenerationTask(
+                task_id=gen_task.task_id,
+                profile_id=gen_task.profile_id,
+                text_preview=gen_task.text_preview,
+                started_at=gen_task.started_at,
+            )
+        )
+
+    return models.ActiveTasksResponse(
+        downloads=active_downloads,
+        generations=active_generations,
+    )

backend/routes/transcription.py

@@ -0,0 +1,84 @@
+"""Transcription endpoints."""
+
+import asyncio
+import tempfile
+from pathlib import Path
+
+from fastapi import APIRouter, File, Form, HTTPException, UploadFile
+
+from .. import models
+from ..services import transcribe
+from ..services.task_queue import create_background_task
+from ..utils.tasks import get_task_manager
+
+router = APIRouter()
+
+UPLOAD_CHUNK_SIZE = 1024 * 1024  # 1MB
+
+
+@router.post("/transcribe", response_model=models.TranscriptionResponse)
+async def transcribe_audio(
+    file: UploadFile = File(...),
+    language: str | None = Form(None),
+    model: str | None = Form(None),
+):
+    """Transcribe audio file to text."""
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
+        while chunk := await file.read(UPLOAD_CHUNK_SIZE):
+            tmp.write(chunk)
+        tmp_path = tmp.name
+
+    try:
+        from ..utils.audio import load_audio
+        from ..backends import WHISPER_HF_REPOS
+
+        audio, sr = await asyncio.to_thread(load_audio, tmp_path)
+        duration = len(audio) / sr
+
+        whisper_model = transcribe.get_whisper_model()
+        model_size = model if model else whisper_model.model_size
+
+        valid_sizes = list(WHISPER_HF_REPOS.keys())
+        if model_size not in valid_sizes:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Invalid model size '{model_size}'. Must be one of: {', '.join(valid_sizes)}",
+            )
+
+        already_loaded = whisper_model.is_loaded() and whisper_model.model_size == model_size
+        if not already_loaded and not whisper_model._is_model_cached(model_size):
+            progress_model_name = f"whisper-{model_size}"
+            task_manager = get_task_manager()
+
+            async def download_whisper_background():
+                try:
+                    await whisper_model.load_model_async(model_size)
+                    task_manager.complete_download(progress_model_name)
+                except Exception as e:
+                    task_manager.error_download(progress_model_name, str(e))
+
+            task_manager.start_download(progress_model_name)
+            create_background_task(download_whisper_background())
+
+            raise HTTPException(
+                status_code=202,
+                detail={
+                    "message": f"Whisper model {model_size} is being downloaded. Please wait and try again.",
+                    "model_name": progress_model_name,
+                    "downloading": True,
+                },
+            )
+
+        text = await whisper_model.transcribe(tmp_path, language, model_size)
+
+        return models.TranscriptionResponse(
+            text=text,
+            duration=duration,
+        )
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+    finally:
+        Path(tmp_path).unlink(missing_ok=True)

backend/server.py

@@ -0,0 +1,303 @@
+"""
+Entry point for PyInstaller-bundled voicebox server.
+
+This module provides an entry point that works with PyInstaller by using
+absolute imports instead of relative imports.
+"""
+
+import sys
+import os
+
+# On Windows with --noconsole (PyInstaller), sys.stdout/stderr are None.
+# They can also be broken file objects in some edge cases.
+# Redirect to devnull to prevent crashes from print()/tqdm/logging.
+def _is_writable(stream):
+    """Check if a stream is usable for writing."""
+    if stream is None:
+        return False
+    try:
+        stream.write("")
+        return True
+    except Exception:
+        return False
+
+if not _is_writable(sys.stdout):
+    sys.stdout = open(os.devnull, 'w')
+if not _is_writable(sys.stderr):
+    sys.stderr = open(os.devnull, 'w')
+
+# PyInstaller + multiprocessing: child processes re-execute the frozen binary
+# with internal arguments. freeze_support() handles this and exits early.
+import multiprocessing
+multiprocessing.freeze_support()
+
+# In frozen builds, piper_phonemize's espeak-ng C library falls back to
+# /usr/share/espeak-ng-data/ which doesn't exist.  Point it at the bundled
+# data directory instead.
+if getattr(sys, 'frozen', False):
+    _meipass = getattr(sys, '_MEIPASS', os.path.dirname(sys.executable))
+    _espeak_data = os.path.join(_meipass, 'piper_phonemize', 'espeak-ng-data')
+    if os.path.isdir(_espeak_data):
+        os.environ.setdefault('ESPEAK_DATA_PATH', _espeak_data)
+
+# Fast path: handle --version before any heavy imports so the Rust
+# version check doesn't block for 30+ seconds loading torch etc.
+if "--version" in sys.argv:
+    from backend import __version__
+    print(f"voicebox-server {__version__}")
+    sys.exit(0)
+
+import logging
+
+# Set up logging FIRST, before any imports that might fail
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    stream=sys.stderr,  # Log to stderr so it's captured by Tauri
+)
+logger = logging.getLogger(__name__)
+
+# Log startup immediately to confirm binary execution
+logger.info("=" * 60)
+logger.info("voicebox-server starting up...")
+logger.info(f"Python version: {sys.version}")
+logger.info(f"Executable: {sys.executable}")
+logger.info(f"Arguments: {sys.argv}")
+logger.info("=" * 60)
+
+try:
+    logger.info("Importing argparse...")
+    import argparse
+    logger.info("Importing uvicorn...")
+    import uvicorn
+    logger.info("Standard library imports successful")
+
+    # Import the FastAPI app from the backend package
+    logger.info("Importing backend.config...")
+    from backend import config
+    logger.info("Importing backend.database...")
+    from backend import database
+    logger.info("Importing backend.main (this may take a while due to torch/transformers)...")
+    from backend.main import app
+    logger.info("Backend imports successful")
+except Exception as e:
+    logger.error(f"Failed to import required modules: {e}", exc_info=True)
+    sys.exit(1)
+
+_watchdog_disabled = False
+
+
+def disable_watchdog():
+    """Disable the parent watchdog so the server keeps running after parent exits."""
+    global _watchdog_disabled
+    _watchdog_disabled = True
+    # Ignore SIGHUP so the server survives when the parent Tauri process exits.
+    # On Unix, child processes receive SIGHUP when the parent's session leader
+    # exits, which would kill the server even though we want it to persist.
+    if sys.platform != "win32":
+        import signal
+        signal.signal(signal.SIGHUP, signal.SIG_IGN)
+
+
+def _start_parent_watchdog(parent_pid, data_dir=None):
+    """Monitor parent process and exit if it dies.
+
+    This is the clean shutdown mechanism: instead of the Tauri app trying to
+    forcefully kill the server (which spawns console windows on Windows),
+    the server monitors its parent and shuts itself down gracefully.
+
+    The Tauri app writes a .keep-running sentinel file to data_dir before
+    exiting when "remain running after close" is enabled. This is a reliable
+    fallback for the HTTP /watchdog/disable request, which can race with
+    process exit on Windows.
+    """
+    import os
+    import signal
+    import threading
+    import time
+
+    # Set up a file logger so we can debug in production
+    watchdog_logger = logging.getLogger("watchdog")
+    if data_dir:
+        try:
+            log_dir = os.path.join(data_dir, "logs")
+            os.makedirs(log_dir, exist_ok=True)
+            fh = logging.FileHandler(os.path.join(log_dir, "watchdog.log"))
+            fh.setFormatter(logging.Formatter('%(asctime)s - %(message)s'))
+            watchdog_logger.addHandler(fh)
+        except Exception:
+            pass
+    watchdog_logger.setLevel(logging.INFO)
+
+    def _is_pid_alive(pid):
+        """Check if a process with the given PID exists (cross-platform)."""
+        try:
+            if sys.platform == "win32":
+                import ctypes
+                kernel32 = ctypes.windll.kernel32
+                PROCESS_QUERY_LIMITED_INFORMATION = 0x1000
+                handle = kernel32.OpenProcess(PROCESS_QUERY_LIMITED_INFORMATION, False, pid)
+                if handle:
+                    # Check if process has actually exited
+                    STILL_ACTIVE = 259
+                    exit_code = ctypes.c_ulong()
+                    result = kernel32.GetExitCodeProcess(handle, ctypes.byref(exit_code))
+                    kernel32.CloseHandle(handle)
+                    if result and exit_code.value == STILL_ACTIVE:
+                        return True
+                    watchdog_logger.info(f"PID {pid}: exited with code {exit_code.value}")
+                    return False
+                # OpenProcess failed — check if it's an access error (process exists
+                # but we can't open it) vs process not found
+                error = ctypes.GetLastError()
+                ACCESS_DENIED = 5
+                if error == ACCESS_DENIED:
+                    return True  # process exists, we just can't open it
+                watchdog_logger.info(f"PID {pid}: OpenProcess failed, error={error}")
+                return False
+            else:
+                os.kill(pid, 0)
+                return True
+        except (OSError, PermissionError):
+            return False
+
+    def _watch():
+        watchdog_logger.info(f"Parent watchdog started, monitoring PID {parent_pid}, server PID {os.getpid()}")
+        # Verify parent is alive before starting the loop
+        alive = _is_pid_alive(parent_pid)
+        watchdog_logger.info(f"Parent PID {parent_pid} initial check: alive={alive}")
+        if not alive:
+            watchdog_logger.warning(f"Parent PID {parent_pid} not found on first check — disabling watchdog")
+            return
+        # Clear any stale .keep-running sentinel from a previous session. The
+        # sentinel is only removed by the watchdog when it's consumed during a
+        # grace period; if the HTTP /watchdog/disable path wins the race on a
+        # "keep running" exit, the sentinel is left on disk. Wipe it here so a
+        # future session can't inherit that stale signal.
+        if data_dir:
+            stale = os.path.join(data_dir, ".keep-running")
+            if os.path.exists(stale):
+                try:
+                    os.remove(stale)
+                    watchdog_logger.info("Removed stale .keep-running sentinel from previous session")
+                except OSError as e:
+                    watchdog_logger.warning(f"Failed to remove stale sentinel: {e}")
+        while True:
+            if _watchdog_disabled:
+                watchdog_logger.info("Watchdog disabled (keep server running), stopping monitor")
+                return
+            if not _is_pid_alive(parent_pid):
+                # Parent is gone. Before shutting down, give the app a moment
+                # to send /watchdog/disable — there is a race where the Tauri
+                # RunEvent::Exit handler sends the disable request while we are
+                # mid-iteration (already past the _watchdog_disabled check above).
+                watchdog_logger.info(f"Parent process {parent_pid} gone, waiting for possible disable request...")
+                time.sleep(1)
+                if _watchdog_disabled:
+                    watchdog_logger.info("Watchdog was disabled during grace period, keeping server alive")
+                    return
+                # Check for sentinel file written by Tauri before exit.
+                # This catches the case where the HTTP disable request
+                # didn't arrive before the parent process died (common
+                # on Windows where process teardown is fast).
+                sentinel = os.path.join(data_dir, ".keep-running") if data_dir else None
+                if sentinel and os.path.exists(sentinel):
+                    watchdog_logger.info("Found .keep-running sentinel file, keeping server alive")
+                    try:
+                        os.remove(sentinel)
+                    except OSError:
+                        pass
+                    return
+                watchdog_logger.info("Watchdog still enabled after grace period, shutting down server...")
+                if sys.platform == "win32":
+                    # sys.exit triggers SystemExit, allowing uvicorn to run
+                    # shutdown handlers. os.kill(SIGTERM) on Windows calls
+                    # TerminateProcess which hard-kills without cleanup.
+                    os._exit(0)
+                else:
+                    os.kill(os.getpid(), signal.SIGTERM)
+                return
+            time.sleep(2)
+
+    t = threading.Thread(target=_watch, daemon=True)
+    t.start()
+
+
+if __name__ == "__main__":
+    try:
+        parser = argparse.ArgumentParser(description="voicebox backend server")
+        parser.add_argument(
+            "--host",
+            type=str,
+            default="127.0.0.1",
+            help="Host to bind to (use 0.0.0.0 for remote access)",
+        )
+        parser.add_argument(
+            "--port",
+            type=int,
+            default=8000,
+            help="Port to bind to",
+        )
+        parser.add_argument(
+            "--data-dir",
+            type=str,
+            default=None,
+            help="Data directory for database, profiles, and generated audio",
+        )
+        parser.add_argument(
+            "--parent-pid",
+            type=int,
+            default=None,
+            help="PID of parent process to monitor; server exits when parent dies",
+        )
+        parser.add_argument(
+            "--version",
+            action="store_true",
+            help="Print version and exit (handled above, kept for argparse help)",
+        )
+        args = parser.parse_args()
+
+        if args.parent_pid is not None and args.parent_pid <= 0:
+            parser.error("--parent-pid must be a positive integer")
+
+        # Detect backend variant from binary name
+        # voicebox-server-cuda → sets VOICEBOX_BACKEND_VARIANT=cuda
+        import os
+        binary_name = os.path.basename(sys.executable).lower()
+        if "cuda" in binary_name:
+            os.environ["VOICEBOX_BACKEND_VARIANT"] = "cuda"
+            logger.info("Backend variant: CUDA")
+        else:
+            os.environ["VOICEBOX_BACKEND_VARIANT"] = "cpu"
+            logger.info("Backend variant: CPU")
+
+        # Register parent watchdog to start after server is fully ready
+        if args.parent_pid is not None:
+            _parent_pid = args.parent_pid
+            _data_dir = args.data_dir
+            @app.on_event("startup")
+            async def _on_startup():
+                _start_parent_watchdog(_parent_pid, _data_dir)
+
+        logger.info(f"Parsed arguments: host={args.host}, port={args.port}, data_dir={args.data_dir}")
+
+        # Set data directory if provided
+        if args.data_dir:
+            logger.info(f"Setting data directory to: {args.data_dir}")
+            config.set_data_dir(args.data_dir)
+
+        # Initialize database after data directory is set
+        logger.info("Initializing database...")
+        database.init_db()
+        logger.info("Database initialized successfully")
+
+        logger.info(f"Starting uvicorn server on {args.host}:{args.port}...")
+        uvicorn.run(
+            app,
+            host=args.host,
+            port=args.port,
+            log_level="info",
+        )
+    except Exception as e:
+        logger.error(f"Server startup failed: {e}", exc_info=True)
+        sys.exit(1)

backend/services/__init__.py

@@ -0,0 +1 @@
+# Services layer — generation orchestration and background task management.

backend/services/channels.py

@@ -0,0 +1,263 @@
+"""
+Audio channel management module.
+"""
+
+from typing import List, Optional
+from datetime import datetime
+import uuid
+from sqlalchemy.orm import Session
+
+from ..models import (
+    AudioChannelCreate,
+    AudioChannelUpdate,
+    AudioChannelResponse,
+    ChannelVoiceAssignment,
+    ProfileChannelAssignment,
+)
+from ..database import (
+    AudioChannel as DBAudioChannel,
+    ChannelDeviceMapping as DBChannelDeviceMapping,
+    ProfileChannelMapping as DBProfileChannelMapping,
+    VoiceProfile as DBVoiceProfile,
+)
+
+
+async def list_channels(db: Session) -> List[AudioChannelResponse]:
+    """List all audio channels."""
+    channels = db.query(DBAudioChannel).all()
+    result = []
+    
+    for channel in channels:
+        # Get device IDs for this channel
+        device_mappings = db.query(DBChannelDeviceMapping).filter_by(
+            channel_id=channel.id
+        ).all()
+        device_ids = [m.device_id for m in device_mappings]
+        
+        result.append(AudioChannelResponse(
+            id=channel.id,
+            name=channel.name,
+            is_default=channel.is_default,
+            device_ids=device_ids,
+            created_at=channel.created_at,
+        ))
+    
+    return result
+
+
+async def get_channel(channel_id: str, db: Session) -> Optional[AudioChannelResponse]:
+    """Get a channel by ID."""
+    channel = db.query(DBAudioChannel).filter_by(id=channel_id).first()
+    if not channel:
+        return None
+    
+    # Get device IDs
+    device_mappings = db.query(DBChannelDeviceMapping).filter_by(
+        channel_id=channel.id
+    ).all()
+    device_ids = [m.device_id for m in device_mappings]
+    
+    return AudioChannelResponse(
+        id=channel.id,
+        name=channel.name,
+        is_default=channel.is_default,
+        device_ids=device_ids,
+        created_at=channel.created_at,
+    )
+
+
+async def create_channel(
+    data: AudioChannelCreate,
+    db: Session,
+) -> AudioChannelResponse:
+    """Create a new audio channel."""
+    # Check if name already exists
+    existing = db.query(DBAudioChannel).filter_by(name=data.name).first()
+    if existing:
+        raise ValueError(f"Channel with name '{data.name}' already exists")
+    
+    # Create channel
+    channel = DBAudioChannel(
+        id=str(uuid.uuid4()),
+        name=data.name,
+        is_default=False,
+        created_at=datetime.utcnow(),
+    )
+    db.add(channel)
+    db.flush()
+    
+    # Add device mappings
+    for device_id in data.device_ids:
+        mapping = DBChannelDeviceMapping(
+            id=str(uuid.uuid4()),
+            channel_id=channel.id,
+            device_id=device_id,
+        )
+        db.add(mapping)
+    
+    db.commit()
+    db.refresh(channel)
+    
+    return AudioChannelResponse(
+        id=channel.id,
+        name=channel.name,
+        is_default=channel.is_default,
+        device_ids=data.device_ids,
+        created_at=channel.created_at,
+    )
+
+
+async def update_channel(
+    channel_id: str,
+    data: AudioChannelUpdate,
+    db: Session,
+) -> Optional[AudioChannelResponse]:
+    """Update an audio channel."""
+    channel = db.query(DBAudioChannel).filter_by(id=channel_id).first()
+    if not channel:
+        return None
+    
+    if channel.is_default:
+        raise ValueError("Cannot modify the default channel")
+    
+    # Update name if provided
+    if data.name is not None:
+        # Check if name already exists (excluding current channel)
+        existing = db.query(DBAudioChannel).filter(
+            DBAudioChannel.name == data.name,
+            DBAudioChannel.id != channel_id
+        ).first()
+        if existing:
+            raise ValueError(f"Channel with name '{data.name}' already exists")
+        channel.name = data.name
+    
+    # Update device mappings if provided
+    if data.device_ids is not None:
+        # Delete existing mappings
+        db.query(DBChannelDeviceMapping).filter_by(channel_id=channel_id).delete()
+        
+        # Add new mappings
+        for device_id in data.device_ids:
+            mapping = DBChannelDeviceMapping(
+                id=str(uuid.uuid4()),
+                channel_id=channel.id,
+                device_id=device_id,
+            )
+            db.add(mapping)
+    
+    db.commit()
+    db.refresh(channel)
+    
+    # Get updated device IDs
+    device_mappings = db.query(DBChannelDeviceMapping).filter_by(
+        channel_id=channel.id
+    ).all()
+    device_ids = [m.device_id for m in device_mappings]
+    
+    return AudioChannelResponse(
+        id=channel.id,
+        name=channel.name,
+        is_default=channel.is_default,
+        device_ids=device_ids,
+        created_at=channel.created_at,
+    )
+
+
+async def delete_channel(channel_id: str, db: Session) -> bool:
+    """Delete an audio channel."""
+    channel = db.query(DBAudioChannel).filter_by(id=channel_id).first()
+    if not channel:
+        return False
+    
+    if channel.is_default:
+        raise ValueError("Cannot delete the default channel")
+    
+    # Delete device mappings
+    db.query(DBChannelDeviceMapping).filter_by(channel_id=channel_id).delete()
+    
+    # Delete profile-channel mappings
+    db.query(DBProfileChannelMapping).filter_by(channel_id=channel_id).delete()
+    
+    # Delete channel
+    db.delete(channel)
+    db.commit()
+    
+    return True
+
+
+async def get_channel_voices(channel_id: str, db: Session) -> List[str]:
+    """Get list of profile IDs assigned to a channel."""
+    mappings = db.query(DBProfileChannelMapping).filter_by(
+        channel_id=channel_id
+    ).all()
+    return [m.profile_id for m in mappings]
+
+
+async def set_channel_voices(
+    channel_id: str,
+    data: ChannelVoiceAssignment,
+    db: Session,
+) -> None:
+    """Set which voices are assigned to a channel."""
+    # Verify channel exists
+    channel = db.query(DBAudioChannel).filter_by(id=channel_id).first()
+    if not channel:
+        raise ValueError(f"Channel {channel_id} not found")
+    
+    # Verify all profiles exist
+    for profile_id in data.profile_ids:
+        profile = db.query(DBVoiceProfile).filter_by(id=profile_id).first()
+        if not profile:
+            raise ValueError(f"Profile {profile_id} not found")
+    
+    # Delete existing mappings for this channel
+    db.query(DBProfileChannelMapping).filter_by(channel_id=channel_id).delete()
+    
+    # Add new mappings
+    for profile_id in data.profile_ids:
+        mapping = DBProfileChannelMapping(
+            profile_id=profile_id,
+            channel_id=channel_id,
+        )
+        db.add(mapping)
+    
+    db.commit()
+
+
+async def get_profile_channels(profile_id: str, db: Session) -> List[str]:
+    """Get list of channel IDs assigned to a profile."""
+    mappings = db.query(DBProfileChannelMapping).filter_by(
+        profile_id=profile_id
+    ).all()
+    return [m.channel_id for m in mappings]
+
+
+async def set_profile_channels(
+    profile_id: str,
+    data: ProfileChannelAssignment,
+    db: Session,
+) -> None:
+    """Set which channels a profile is assigned to."""
+    # Verify profile exists
+    profile = db.query(DBVoiceProfile).filter_by(id=profile_id).first()
+    if not profile:
+        raise ValueError(f"Profile {profile_id} not found")
+    
+    # Verify all channels exist
+    for channel_id in data.channel_ids:
+        channel = db.query(DBAudioChannel).filter_by(id=channel_id).first()
+        if not channel:
+            raise ValueError(f"Channel {channel_id} not found")
+    
+    # Delete existing mappings for this profile
+    db.query(DBProfileChannelMapping).filter_by(profile_id=profile_id).delete()
+    
+    # Add new mappings
+    for channel_id in data.channel_ids:
+        mapping = DBProfileChannelMapping(
+            profile_id=profile_id,
+            channel_id=channel_id,
+        )
+        db.add(mapping)
+    
+    db.commit()

backend/services/cuda.py

@@ -0,0 +1,422 @@
+"""
+CUDA backend download, assembly, and verification.
+
+Downloads two archives from GitHub Releases:
+  1. Server core (voicebox-server-cuda.tar.gz) — the exe + non-NVIDIA deps,
+     versioned with the app.
+  2. CUDA libs (cuda-libs-{version}.tar.gz) — NVIDIA runtime libraries,
+     versioned independently (only redownloaded on CUDA toolkit bump).
+
+Both archives are extracted into {data_dir}/backends/cuda/ which forms the
+complete PyInstaller --onedir directory structure that torch expects.
+"""
+
+import asyncio
+import hashlib
+import json
+import logging
+import os
+import sys
+import tarfile
+from pathlib import Path
+from typing import Optional
+
+from ..config import get_data_dir
+from ..utils.progress import get_progress_manager
+from .. import __version__
+
+logger = logging.getLogger(__name__)
+
+GITHUB_RELEASES_URL = "https://github.com/jamiepine/voicebox/releases/download"
+
+PROGRESS_KEY = "cuda-backend"
+
+# The current expected CUDA libs version.  Bump this when we change the
+# CUDA toolkit version or torch's CUDA dependency changes (e.g. cu126 -> cu128).
+CUDA_LIBS_VERSION = "cu128-v1"
+
+# Prevents concurrent download_cuda_binary() calls from racing on the same
+# temp file.  The auto-update background task and the manual HTTP endpoint
+# can both invoke download_cuda_binary(); without this lock the progress-
+# manager status check is a TOCTOU race.
+_download_lock = asyncio.Lock()
+
+
+def get_backends_dir() -> Path:
+    """Directory where downloaded backend binaries are stored."""
+    d = get_data_dir() / "backends"
+    d.mkdir(parents=True, exist_ok=True)
+    return d
+
+
+def get_cuda_dir() -> Path:
+    """Directory where the CUDA backend (onedir) is extracted."""
+    d = get_backends_dir() / "cuda"
+    d.mkdir(parents=True, exist_ok=True)
+    return d
+
+
+def get_cuda_exe_name() -> str:
+    """Platform-specific CUDA executable filename."""
+    if sys.platform == "win32":
+        return "voicebox-server-cuda.exe"
+    return "voicebox-server-cuda"
+
+
+def get_cuda_binary_path() -> Optional[Path]:
+    """Return path to the CUDA executable if it exists inside the onedir."""
+    p = get_cuda_dir() / get_cuda_exe_name()
+    if p.exists():
+        return p
+    return None
+
+
+def get_cuda_libs_manifest_path() -> Path:
+    """Path to the cuda-libs.json manifest inside the CUDA dir."""
+    return get_cuda_dir() / "cuda-libs.json"
+
+
+def get_installed_cuda_libs_version() -> Optional[str]:
+    """Read the installed CUDA libs version from cuda-libs.json, or None."""
+    manifest_path = get_cuda_libs_manifest_path()
+    if not manifest_path.exists():
+        return None
+    try:
+        data = json.loads(manifest_path.read_text())
+        return data.get("version")
+    except Exception as e:
+        logger.warning(f"Could not read cuda-libs.json: {e}")
+        return None
+
+
+def is_cuda_active() -> bool:
+    """Check if the current process is the CUDA binary.
+
+    The CUDA binary sets this env var on startup (see server.py).
+    """
+    return os.environ.get("VOICEBOX_BACKEND_VARIANT") == "cuda"
+
+
+def get_cuda_status() -> dict:
+    """Get current CUDA backend status for the API."""
+    progress_manager = get_progress_manager()
+    cuda_path = get_cuda_binary_path()
+    progress = progress_manager.get_progress(PROGRESS_KEY)
+    cuda_libs_version = get_installed_cuda_libs_version()
+
+    return {
+        "available": cuda_path is not None,
+        "active": is_cuda_active(),
+        "binary_path": str(cuda_path) if cuda_path else None,
+        "cuda_libs_version": cuda_libs_version,
+        "downloading": progress is not None and progress.get("status") == "downloading",
+        "download_progress": progress,
+    }
+
+
+def _needs_server_download(version: Optional[str] = None) -> bool:
+    """Check if the server core archive needs to be (re)downloaded."""
+    cuda_path = get_cuda_binary_path()
+    if not cuda_path:
+        return True
+    # Check if the binary version matches the expected app version
+    installed = get_cuda_binary_version()
+    expected = version or __version__
+    if expected.startswith("v"):
+        expected = expected[1:]
+    return installed != expected
+
+
+def _needs_cuda_libs_download() -> bool:
+    """Check if the CUDA libs archive needs to be (re)downloaded."""
+    installed = get_installed_cuda_libs_version()
+    if installed is None:
+        return True
+    return installed != CUDA_LIBS_VERSION
+
+
+async def _download_and_extract_archive(
+    client,
+    url: str,
+    sha256_url: Optional[str],
+    dest_dir: Path,
+    label: str,
+    progress_offset: int,
+    total_size: int,
+):
+    """Download a .tar.gz archive and extract it into dest_dir.
+
+    Args:
+        client: httpx.AsyncClient
+        url: URL of the .tar.gz archive
+        sha256_url: URL of the .sha256 checksum file (optional)
+        dest_dir: Directory to extract into
+        label: Human-readable label for progress updates
+        progress_offset: Byte offset for progress reporting (when downloading
+            multiple archives sequentially)
+        total_size: Total bytes across all downloads (for progress bar)
+    """
+    progress = get_progress_manager()
+    temp_path = dest_dir / f".download-{label.replace(' ', '-')}.tmp"
+
+    # Clean up leftover partial download
+    if temp_path.exists():
+        temp_path.unlink()
+
+    # Fetch expected checksum (fail-fast: never extract an unverified archive)
+    expected_sha = None
+    if sha256_url:
+        try:
+            sha_resp = await client.get(sha256_url)
+            sha_resp.raise_for_status()
+            expected_sha = sha_resp.text.strip().split()[0]
+            logger.info(f"{label}: expected SHA-256: {expected_sha[:16]}...")
+        except Exception as e:
+            raise RuntimeError(f"{label}: failed to fetch checksum from {sha256_url}") from e
+
+    # Stream download, verify, and extract — always clean up temp file
+    downloaded = 0
+    try:
+        async with client.stream("GET", url) as response:
+            response.raise_for_status()
+            with open(temp_path, "wb") as f:
+                async for chunk in response.aiter_bytes(chunk_size=1024 * 1024):
+                    f.write(chunk)
+                    downloaded += len(chunk)
+                    progress.update_progress(
+                        PROGRESS_KEY,
+                        current=progress_offset + downloaded,
+                        total=total_size,
+                        filename=f"Downloading {label}",
+                        status="downloading",
+                    )
+
+        # Verify integrity
+        if expected_sha:
+            progress.update_progress(
+                PROGRESS_KEY,
+                current=progress_offset + downloaded,
+                total=total_size,
+                filename=f"Verifying {label}...",
+                status="downloading",
+            )
+            sha256 = hashlib.sha256()
+            with open(temp_path, "rb") as f:
+                while True:
+                    data = f.read(1024 * 1024)
+                    if not data:
+                        break
+                    sha256.update(data)
+            actual = sha256.hexdigest()
+            if actual != expected_sha:
+                raise ValueError(
+                    f"{label} integrity check failed: expected {expected_sha[:16]}..., got {actual[:16]}..."
+                )
+            logger.info(f"{label}: integrity verified")
+
+        # Extract (use data filter for path traversal protection on Python 3.12+)
+        progress.update_progress(
+            PROGRESS_KEY,
+            current=progress_offset + downloaded,
+            total=total_size,
+            filename=f"Extracting {label}...",
+            status="downloading",
+        )
+        with tarfile.open(temp_path, "r:gz") as tar:
+            if sys.version_info >= (3, 12):
+                tar.extractall(path=dest_dir, filter="data")
+            else:
+                tar.extractall(path=dest_dir)
+
+        logger.info(f"{label}: extracted to {dest_dir}")
+    finally:
+        if temp_path.exists():
+            temp_path.unlink()
+    return downloaded
+
+
+async def download_cuda_binary(version: Optional[str] = None):
+    """Download the CUDA backend (server core + CUDA libs if needed).
+
+    Downloads both archives from GitHub Releases, extracts them into
+    {data_dir}/backends/cuda/, and writes the cuda-libs.json manifest.
+
+    Only downloads what's needed:
+    - Server core: always redownloaded (versioned with app)
+    - CUDA libs: only if missing or version mismatch
+
+    Args:
+        version: Version tag (e.g. "v0.3.0"). Defaults to current app version.
+    """
+    if _download_lock.locked():
+        logger.info("CUDA download already in progress, skipping duplicate request")
+        return
+    async with _download_lock:
+        await _download_cuda_binary_locked(version)
+
+
+async def _download_cuda_binary_locked(version: Optional[str] = None):
+    """Inner implementation of download_cuda_binary, called under _download_lock."""
+    import httpx
+
+    if version is None:
+        version = f"v{__version__}"
+
+    progress = get_progress_manager()
+    cuda_dir = get_cuda_dir()
+
+    need_server = _needs_server_download(version)
+    need_libs = _needs_cuda_libs_download()
+
+    if not need_server and not need_libs:
+        logger.info("CUDA backend is up to date, nothing to download")
+        return
+
+    logger.info(
+        f"Starting CUDA backend download for {version} "
+        f"(server={'yes' if need_server else 'cached'}, "
+        f"libs={'yes' if need_libs else 'cached'})"
+    )
+    progress.update_progress(
+        PROGRESS_KEY,
+        current=0,
+        total=0,
+        filename="Preparing download...",
+        status="downloading",
+    )
+
+    base_url = f"{GITHUB_RELEASES_URL}/{version}"
+    server_archive = "voicebox-server-cuda.tar.gz"
+    libs_archive = f"cuda-libs-{CUDA_LIBS_VERSION}.tar.gz"
+
+    try:
+        async with httpx.AsyncClient(follow_redirects=True, timeout=30.0) as client:
+            # Estimate total download size
+            total_size = 0
+            if need_server:
+                try:
+                    head = await client.head(f"{base_url}/{server_archive}")
+                    total_size += int(head.headers.get("content-length", 0))
+                except Exception:
+                    pass
+            if need_libs:
+                try:
+                    head = await client.head(f"{base_url}/{libs_archive}")
+                    total_size += int(head.headers.get("content-length", 0))
+                except Exception:
+                    pass
+
+            logger.info(f"Total download size: {total_size / 1024 / 1024:.1f} MB")
+
+            offset = 0
+
+            # Download server core
+            if need_server:
+                server_downloaded = await _download_and_extract_archive(
+                    client,
+                    url=f"{base_url}/{server_archive}",
+                    sha256_url=f"{base_url}/{server_archive}.sha256",
+                    dest_dir=cuda_dir,
+                    label="CUDA server",
+                    progress_offset=offset,
+                    total_size=total_size,
+                )
+                offset += server_downloaded
+
+                # Make executable on Unix
+                exe_path = cuda_dir / get_cuda_exe_name()
+                if sys.platform != "win32" and exe_path.exists():
+                    exe_path.chmod(0o755)
+
+            # Download CUDA libs
+            if need_libs:
+                await _download_and_extract_archive(
+                    client,
+                    url=f"{base_url}/{libs_archive}",
+                    sha256_url=f"{base_url}/{libs_archive}.sha256",
+                    dest_dir=cuda_dir,
+                    label="CUDA libraries",
+                    progress_offset=offset,
+                    total_size=total_size,
+                )
+
+                # Write local cuda-libs.json manifest
+                manifest = {"version": CUDA_LIBS_VERSION}
+                get_cuda_libs_manifest_path().write_text(json.dumps(manifest, indent=2) + "\n")
+
+        logger.info(f"CUDA backend ready at {cuda_dir}")
+        progress.mark_complete(PROGRESS_KEY)
+
+    except Exception as e:
+        logger.error(f"CUDA backend download failed: {e}")
+        progress.mark_error(PROGRESS_KEY, str(e))
+        raise
+
+
+def get_cuda_binary_version() -> Optional[str]:
+    """Get the version of the installed CUDA binary, or None if not installed."""
+    import subprocess
+
+    cuda_path = get_cuda_binary_path()
+    if not cuda_path:
+        return None
+    try:
+        result = subprocess.run(
+            [str(cuda_path), "--version"],
+            capture_output=True,
+            text=True,
+            timeout=30,
+            cwd=str(cuda_path.parent),  # Run from the onedir directory
+        )
+        # Output format: "voicebox-server 0.3.0"
+        for line in result.stdout.strip().splitlines():
+            if "voicebox-server" in line:
+                return line.split()[-1]
+    except Exception as e:
+        logger.warning(f"Could not get CUDA binary version: {e}")
+    return None
+
+
+async def check_and_update_cuda_binary():
+    """Check if the CUDA binary is outdated and auto-download if so.
+
+    Called on server startup. Checks both server version and CUDA libs
+    version. Downloads only what's needed.
+    """
+    cuda_path = get_cuda_binary_path()
+    if not cuda_path:
+        return  # No CUDA binary installed, nothing to update
+
+    need_server = _needs_server_download()
+    need_libs = _needs_cuda_libs_download()
+
+    if not need_server and not need_libs:
+        logger.info(f"CUDA binary is up to date (server=v{__version__}, libs={get_installed_cuda_libs_version()})")
+        return
+
+    reasons = []
+    if need_server:
+        cuda_version = get_cuda_binary_version()
+        reasons.append(f"server v{cuda_version} != v{__version__}")
+    if need_libs:
+        installed_libs = get_installed_cuda_libs_version()
+        reasons.append(f"libs {installed_libs} != {CUDA_LIBS_VERSION}")
+
+    logger.info(f"CUDA backend needs update ({', '.join(reasons)}). Auto-downloading...")
+
+    try:
+        await download_cuda_binary()
+    except Exception as e:
+        logger.error(f"Auto-update of CUDA binary failed: {e}")
+
+
+async def delete_cuda_binary() -> bool:
+    """Delete the downloaded CUDA backend directory. Returns True if deleted."""
+    import shutil
+
+    cuda_dir = get_cuda_dir()
+    if cuda_dir.exists() and any(cuda_dir.iterdir()):
+        shutil.rmtree(cuda_dir)
+        logger.info(f"Deleted CUDA backend directory: {cuda_dir}")
+        return True
+    return False

backend/services/effects.py

@@ -0,0 +1,120 @@
+"""
+Effect presets CRUD operations.
+"""
+
+from __future__ import annotations
+
+import json
+import uuid
+from typing import List, Optional
+
+from sqlalchemy.orm import Session
+from sqlalchemy.exc import IntegrityError
+
+from ..utils.effects import validate_effects_chain
+
+from ..database import EffectPreset as DBEffectPreset
+from ..models import EffectPresetResponse, EffectPresetCreate, EffectPresetUpdate, EffectConfig
+
+
+def _preset_response(p: DBEffectPreset) -> EffectPresetResponse:
+    """Convert a DB preset row to a Pydantic response."""
+    effects_chain = [EffectConfig(**e) for e in json.loads(p.effects_chain)]
+    return EffectPresetResponse(
+        id=p.id,
+        name=p.name,
+        description=p.description,
+        effects_chain=effects_chain,
+        is_builtin=p.is_builtin or False,
+        created_at=p.created_at,
+    )
+
+
+def list_presets(db: Session) -> List[EffectPresetResponse]:
+    """List all effect presets (built-in + user-created)."""
+    presets = db.query(DBEffectPreset).order_by(DBEffectPreset.sort_order, DBEffectPreset.name).all()
+    return [_preset_response(p) for p in presets]
+
+
+def get_preset(preset_id: str, db: Session) -> Optional[EffectPresetResponse]:
+    """Get a preset by ID."""
+    p = db.query(DBEffectPreset).filter_by(id=preset_id).first()
+    if not p:
+        return None
+    return _preset_response(p)
+
+
+def get_preset_by_name(name: str, db: Session) -> Optional[EffectPresetResponse]:
+    """Get a preset by name."""
+    p = db.query(DBEffectPreset).filter_by(name=name).first()
+    if not p:
+        return None
+    return _preset_response(p)
+
+
+def create_preset(data: EffectPresetCreate, db: Session) -> EffectPresetResponse:
+    """Create a new user effect preset."""
+
+    chain_dicts = [e.model_dump() for e in data.effects_chain]
+    error = validate_effects_chain(chain_dicts)
+    if error:
+        raise ValueError(error)
+
+    # Check for duplicate name before insert
+    existing = db.query(DBEffectPreset).filter_by(name=data.name).first()
+    if existing:
+        raise ValueError(f"A preset named '{data.name}' already exists")
+
+    preset = DBEffectPreset(
+        id=str(uuid.uuid4()),
+        name=data.name,
+        description=data.description,
+        effects_chain=json.dumps(chain_dicts),
+        is_builtin=False,
+    )
+    db.add(preset)
+    try:
+        db.commit()
+    except IntegrityError:
+        db.rollback()
+        raise ValueError(f"A preset named '{data.name}' already exists")
+    db.refresh(preset)
+    return _preset_response(preset)
+
+
+def update_preset(preset_id: str, data: EffectPresetUpdate, db: Session) -> Optional[EffectPresetResponse]:
+    """Update a user effect preset. Cannot modify built-in presets."""
+    preset = db.query(DBEffectPreset).filter_by(id=preset_id).first()
+    if not preset:
+        return None
+    if preset.is_builtin:
+        raise ValueError("Cannot modify built-in presets")
+
+    if data.name is not None:
+        preset.name = data.name
+    if data.description is not None:
+        preset.description = data.description
+    if data.effects_chain is not None:
+
+        chain_dicts = [e.model_dump() for e in data.effects_chain]
+        error = validate_effects_chain(chain_dicts)
+        if error:
+            raise ValueError(error)
+        preset.effects_chain = json.dumps(chain_dicts)
+
+    db.commit()
+    db.refresh(preset)
+    return _preset_response(preset)
+
+
+def delete_preset(preset_id: str, db: Session) -> bool:
+    """Delete a user effect preset. Cannot delete built-in presets."""
+    preset = db.query(DBEffectPreset).filter_by(id=preset_id).first()
+    if not preset:
+        return False
+    if preset.is_builtin:
+        raise ValueError("Cannot delete built-in presets")
+
+    db.delete(preset)
+    db.commit()
+    return True

backend/services/export_import.py

@@ -0,0 +1,461 @@
+"""
+Voice profile export/import module.
+
+Handles exporting profiles to ZIP archives and importing them back.
+Also handles exporting individual generations.
+"""
+
+import json
+import zipfile
+import io
+from pathlib import Path
+from typing import Optional
+from sqlalchemy.orm import Session
+
+from ..models import VoiceProfileResponse
+from ..database import VoiceProfile as DBVoiceProfile, ProfileSample as DBProfileSample, Generation as DBGeneration, GenerationVersion as DBGenerationVersion
+from .profiles import create_profile, add_profile_sample
+from ..models import VoiceProfileCreate
+from .. import config
+
+
+def _get_unique_profile_name(name: str, db: Session) -> str:
+    """
+    Get a unique profile name by appending a number if needed.
+    
+    Args:
+        name: Original profile name
+        db: Database session
+        
+    Returns:
+        Unique profile name
+    """
+    base_name = name
+    counter = 1
+    
+    while True:
+        existing = db.query(DBVoiceProfile).filter_by(name=name).first()
+        if not existing:
+            return name
+        
+        name = f"{base_name} ({counter})"
+        counter += 1
+
+
+def export_profile_to_zip(profile_id: str, db: Session) -> bytes:
+    """
+    Export a voice profile to a ZIP archive.
+    
+    Args:
+        profile_id: Profile ID to export
+        db: Database session
+        
+    Returns:
+        ZIP file contents as bytes
+        
+    Raises:
+        ValueError: If profile not found or has no samples
+    """
+    # Get profile
+    profile = db.query(DBVoiceProfile).filter_by(id=profile_id).first()
+    if not profile:
+        raise ValueError(f"Profile {profile_id} not found")
+    
+    # Get all samples
+    samples = db.query(DBProfileSample).filter_by(profile_id=profile_id).all()
+    if not samples:
+        raise ValueError(f"Profile {profile_id} has no samples")
+    
+    # Create ZIP in memory
+    zip_buffer = io.BytesIO()
+    
+    with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zip_file:
+        # Check if profile has avatar
+        has_avatar = False
+        if profile.avatar_path:
+            avatar_path = config.resolve_storage_path(profile.avatar_path)
+            if avatar_path is not None and avatar_path.exists():
+                has_avatar = True
+                # Add avatar to ZIP root with original extension
+                avatar_ext = avatar_path.suffix
+                zip_file.write(avatar_path, f"avatar{avatar_ext}")
+
+        # Create manifest.json
+        manifest = {
+            "version": "1.0",
+            "profile": {
+                "name": profile.name,
+                "description": profile.description,
+                "language": profile.language,
+            },
+            "has_avatar": has_avatar,
+        }
+        zip_file.writestr("manifest.json", json.dumps(manifest, indent=2))
+
+        # Create samples.json mapping
+        samples_data = {}
+        profile_dir = config.get_profiles_dir() / profile_id
+
+        for sample in samples:
+            # Get filename from audio_path (should be {sample_id}.wav)
+            audio_path = config.resolve_storage_path(sample.audio_path)
+            if audio_path is None:
+                raise ValueError(f"Audio file not found: {sample.audio_path}")
+            filename = audio_path.name
+
+            # Read audio file
+            if not audio_path.exists():
+                raise ValueError(f"Audio file not found: {audio_path}")
+
+            # Add to samples directory in ZIP
+            zip_path = f"samples/{filename}"
+            zip_file.write(audio_path, zip_path)
+
+            # Map filename to reference text
+            samples_data[filename] = sample.reference_text
+
+        zip_file.writestr("samples.json", json.dumps(samples_data, indent=2))
+    
+    zip_buffer.seek(0)
+    return zip_buffer.read()
+
+
+async def import_profile_from_zip(file_bytes: bytes, db: Session) -> VoiceProfileResponse:
+    """
+    Import a voice profile from a ZIP archive.
+    
+    Args:
+        file_bytes: ZIP file contents
+        db: Database session
+        
+    Returns:
+        Created profile
+        
+    Raises:
+        ValueError: If ZIP is invalid or missing required files
+    """
+    zip_buffer = io.BytesIO(file_bytes)
+    
+    try:
+        with zipfile.ZipFile(zip_buffer, 'r') as zip_file:
+            # Validate ZIP structure
+            namelist = zip_file.namelist()
+            
+            if "manifest.json" not in namelist:
+                raise ValueError("ZIP archive missing manifest.json")
+            
+            if "samples.json" not in namelist:
+                raise ValueError("ZIP archive missing samples.json")
+            
+            # Read manifest
+            manifest_data = json.loads(zip_file.read("manifest.json"))
+            
+            if "version" not in manifest_data:
+                raise ValueError("Invalid manifest.json: missing version")
+            
+            if "profile" not in manifest_data:
+                raise ValueError("Invalid manifest.json: missing profile")
+            
+            profile_data = manifest_data["profile"]
+            
+            # Read samples mapping
+            samples_data = json.loads(zip_file.read("samples.json"))
+            
+            if not isinstance(samples_data, dict):
+                raise ValueError("Invalid samples.json: must be a dictionary")
+            
+            # Get unique profile name
+            original_name = profile_data.get("name", "Imported Profile")
+            unique_name = _get_unique_profile_name(original_name, db)
+            
+            # Create profile
+            profile_create = VoiceProfileCreate(
+                name=unique_name,
+                description=profile_data.get("description"),
+                language=profile_data.get("language", "en"),
+            )
+            
+            profile = await create_profile(profile_create, db)
+
+            # Extract and add samples
+            profile_dir = config.get_profiles_dir() / profile.id
+            profile_dir.mkdir(parents=True, exist_ok=True)
+
+            # Handle avatar if present
+            avatar_files = [f for f in namelist if f.startswith("avatar.")]
+            if avatar_files:
+                try:
+                    avatar_file = avatar_files[0]
+                    # Extract to temporary file
+                    import tempfile
+                    with tempfile.NamedTemporaryFile(suffix=Path(avatar_file).suffix, delete=False) as tmp:
+                        tmp.write(zip_file.read(avatar_file))
+                        tmp_path = tmp.name
+
+                    try:
+                        from .profiles import upload_avatar
+                        await upload_avatar(profile.id, tmp_path, db)
+                    finally:
+                        Path(tmp_path).unlink(missing_ok=True)
+                except Exception as e:
+                    # Avatar import is optional - continue even if it fails
+                    pass
+
+            for filename, reference_text in samples_data.items():
+                # Validate filename
+                if not filename.endswith('.wav'):
+                    raise ValueError(f"Invalid sample filename: {filename} (must be .wav)")
+                
+                # Extract audio file to temp location
+                zip_path = f"samples/{filename}"
+                
+                if zip_path not in namelist:
+                    raise ValueError(f"Sample file not found in ZIP: {zip_path}")
+                
+                # Extract to temporary file
+                import tempfile
+                with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
+                    tmp.write(zip_file.read(zip_path))
+                    tmp_path = tmp.name
+                
+                try:
+                    # Add sample to profile
+                    await add_profile_sample(
+                        profile.id,
+                        tmp_path,
+                        reference_text,
+                        db,
+                    )
+                finally:
+                    # Clean up temp file
+                    Path(tmp_path).unlink(missing_ok=True)
+            
+            return profile
+            
+    except zipfile.BadZipFile:
+        raise ValueError("Invalid ZIP file")
+    except json.JSONDecodeError as e:
+        raise ValueError(f"Invalid JSON in archive: {e}")
+    except Exception as e:
+        if isinstance(e, ValueError):
+            raise
+        raise ValueError(f"Error importing profile: {str(e)}")
+
+
+def export_generation_to_zip(generation_id: str, db: Session) -> bytes:
+    """
+    Export a generation to a ZIP archive.
+    
+    Args:
+        generation_id: Generation ID to export
+        db: Database session
+        
+    Returns:
+        ZIP file contents as bytes
+        
+    Raises:
+        ValueError: If generation not found
+    """
+    # Get generation
+    generation = db.query(DBGeneration).filter_by(id=generation_id).first()
+    if not generation:
+        raise ValueError(f"Generation {generation_id} not found")
+    
+    # Get profile info
+    profile = db.query(DBVoiceProfile).filter_by(id=generation.profile_id).first()
+    if not profile:
+        raise ValueError(f"Profile {generation.profile_id} not found")
+    
+    # Get all versions for this generation
+    versions = (
+        db.query(DBGenerationVersion)
+        .filter_by(generation_id=generation_id)
+        .order_by(DBGenerationVersion.created_at)
+        .all()
+    )
+
+    # Create ZIP in memory
+    zip_buffer = io.BytesIO()
+    
+    with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zip_file:
+        # Build version manifest entries
+        version_entries = []
+        for v in versions:
+            v_path = config.resolve_storage_path(v.audio_path)
+            effects_chain = None
+            if v.effects_chain:
+                effects_chain = json.loads(v.effects_chain)
+            version_entries.append({
+                "id": v.id,
+                "label": v.label,
+                "is_default": v.is_default,
+                "effects_chain": effects_chain,
+                "filename": v_path.name,
+            })
+
+        manifest = {
+            "version": "1.0",
+            "generation": {
+                "id": generation.id,
+                "text": generation.text,
+                "language": generation.language,
+                "duration": generation.duration,
+                "seed": generation.seed,
+                "instruct": generation.instruct,
+                "created_at": generation.created_at.isoformat(),
+            },
+            "profile": {
+                "id": profile.id,
+                "name": profile.name,
+                "description": profile.description,
+                "language": profile.language,
+            },
+            "versions": version_entries,
+        }
+        zip_file.writestr("manifest.json", json.dumps(manifest, indent=2))
+        
+        # Add all version audio files
+        for v in versions:
+            v_path = config.resolve_storage_path(v.audio_path)
+            if v_path is not None and v_path.exists():
+                zip_file.write(v_path, f"audio/{v_path.name}")
+
+        # Fallback: if no versions exist, include the generation's main audio
+        if not versions:
+            audio_path = config.resolve_storage_path(generation.audio_path)
+            if audio_path is not None and audio_path.exists():
+                zip_file.write(audio_path, f"audio/{audio_path.name}")
+    
+    zip_buffer.seek(0)
+    return zip_buffer.read()
+
+
+async def import_generation_from_zip(file_bytes: bytes, db: Session) -> dict:
+    """
+    Import a generation from a ZIP archive.
+    
+    Args:
+        file_bytes: ZIP file contents
+        db: Database session
+        
+    Returns:
+        Dictionary with generation ID and profile info
+        
+    Raises:
+        ValueError: If ZIP is invalid or missing required files
+    """
+    from pathlib import Path
+    import tempfile
+    import shutil
+    from datetime import datetime
+    from .. import config
+    
+    zip_buffer = io.BytesIO(file_bytes)
+    
+    try:
+        with zipfile.ZipFile(zip_buffer, 'r') as zip_file:
+            # Validate ZIP structure
+            namelist = zip_file.namelist()
+            
+            if "manifest.json" not in namelist:
+                raise ValueError("ZIP archive missing manifest.json")
+            
+            # Read manifest
+            manifest_data = json.loads(zip_file.read("manifest.json"))
+            
+            if "version" not in manifest_data:
+                raise ValueError("Invalid manifest.json: missing version")
+            
+            if "generation" not in manifest_data:
+                raise ValueError("Invalid manifest.json: missing generation data")
+            
+            generation_data = manifest_data["generation"]
+            profile_data = manifest_data.get("profile", {})
+            
+            # Validate required fields
+            required_fields = ["text", "language", "duration"]
+            for field in required_fields:
+                if field not in generation_data:
+                    raise ValueError(f"Invalid manifest.json: missing generation.{field}")
+            
+            # Find audio file in archive
+            audio_files = [f for f in namelist if f.startswith("audio/") and f.endswith(".wav")]
+            if not audio_files:
+                raise ValueError("No audio file found in ZIP archive")
+            
+            audio_file_path = audio_files[0]
+            
+            # Check if we should match an existing profile or create metadata
+            profile_id = None
+            profile_name = profile_data.get("name", "Unknown Profile")
+            
+            # Try to find matching profile by name
+            if profile_name and profile_name != "Unknown Profile":
+                existing_profile = db.query(DBVoiceProfile).filter_by(name=profile_name).first()
+                if existing_profile:
+                    profile_id = existing_profile.id
+            
+            # If no matching profile, use a placeholder or the first available profile
+            if not profile_id:
+                # Get any profile, or None if no profiles exist
+                any_profile = db.query(DBVoiceProfile).first()
+                if any_profile:
+                    profile_id = any_profile.id
+                    profile_name = any_profile.name
+                else:
+                    raise ValueError("No voice profiles found. Please create a profile before importing generations.")
+            
+            # Extract audio file to temporary location
+            with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
+                tmp.write(zip_file.read(audio_file_path))
+                tmp_path = tmp.name
+            
+            try:
+                # Create generations directory
+                generations_dir = config.get_generations_dir()
+                generations_dir.mkdir(parents=True, exist_ok=True)
+                
+                # Generate new ID for this generation
+                new_generation_id = str(__import__('uuid').uuid4())
+                
+                # Copy audio to generations directory
+                audio_dest = generations_dir / f"{new_generation_id}.wav"
+                shutil.copy(tmp_path, audio_dest)
+                
+                # Create generation record
+                db_generation = DBGeneration(
+                    id=new_generation_id,
+                    profile_id=profile_id,
+                    text=generation_data["text"],
+                    language=generation_data["language"],
+                    audio_path=config.to_storage_path(audio_dest),
+                    duration=generation_data["duration"],
+                    seed=generation_data.get("seed"),
+                    instruct=generation_data.get("instruct"),
+                    created_at=datetime.utcnow(),
+                )
+                
+                db.add(db_generation)
+                db.commit()
+                db.refresh(db_generation)
+                
+                return {
+                    "id": db_generation.id,
+                    "profile_id": profile_id,
+                    "profile_name": profile_name,
+                    "text": db_generation.text,
+                    "message": f"Generation imported successfully (assigned to profile: {profile_name})"
+                }
+                
+            finally:
+                # Clean up temp file
+                Path(tmp_path).unlink(missing_ok=True)
+            
+    except zipfile.BadZipFile:
+        raise ValueError("Invalid ZIP file")
+    except json.JSONDecodeError as e:
+        raise ValueError(f"Invalid JSON in archive: {e}")
+    except Exception as e:
+        if isinstance(e, ValueError):
+            raise
+        raise ValueError(f"Error importing generation: {str(e)}")

backend/services/generation.py

@@ -0,0 +1,263 @@
+"""
+Unified TTS generation orchestration.
+
+Replaces the three near-identical closures (_run_generation, _run_retry,
+_run_regenerate) that lived in main.py with a single ``run_generation()``
+function parameterized by *mode*.
+
+Mode differences:
+  - "generate"   : full pipeline -- save clean version, optionally apply
+                    effects and create a processed version.
+  - "retry"      : re-runs a failed generation with the same seed.
+                    No effects, no version creation.
+  - "regenerate" : re-runs with seed=None for variation.  Creates a new
+                    version with an auto-incremented "take-N" label.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import traceback
+from typing import Literal, Optional
+
+from .. import config
+from . import history, profiles
+from ..database import get_db
+from ..utils.tasks import get_task_manager
+
+
+async def run_generation(
+    *,
+    generation_id: str,
+    profile_id: str,
+    text: str,
+    language: str,
+    engine: str,
+    model_size: str,
+    seed: Optional[int],
+    normalize: bool = False,
+    effects_chain: Optional[list] = None,
+    instruct: Optional[str] = None,
+    mode: Literal["generate", "retry", "regenerate"],
+    max_chunk_chars: Optional[int] = None,
+    crossfade_ms: Optional[int] = None,
+    version_id: Optional[str] = None,
+) -> None:
+    """Execute TTS inference and persist the result.
+
+    This is the single entry point for all background generation work.
+    It is designed to be enqueued via ``services.task_queue.enqueue_generation``.
+    """
+    from ..backends import load_engine_model, get_tts_backend_for_engine, engine_needs_trim
+    from ..utils.chunked_tts import generate_chunked
+    from ..utils.audio import normalize_audio, save_audio, trim_tts_output
+
+    task_manager = get_task_manager()
+    bg_db = next(get_db())
+
+    try:
+        tts_model = get_tts_backend_for_engine(engine)
+
+        if not tts_model.is_loaded():
+            await history.update_generation_status(generation_id, "loading_model", bg_db)
+
+        await load_engine_model(engine, model_size)
+
+        voice_prompt = await profiles.create_voice_prompt_for_profile(
+            profile_id,
+            bg_db,
+            use_cache=True,
+            engine=engine,
+        )
+
+        await history.update_generation_status(generation_id, "generating", bg_db)
+        trim_fn = trim_tts_output if engine_needs_trim(engine) else None
+
+        gen_kwargs: dict = dict(
+            language=language,
+            seed=seed if mode != "regenerate" else None,
+            instruct=instruct,
+            trim_fn=trim_fn,
+        )
+        if max_chunk_chars is not None:
+            gen_kwargs["max_chunk_chars"] = max_chunk_chars
+        if crossfade_ms is not None:
+            gen_kwargs["crossfade_ms"] = crossfade_ms
+
+        audio, sample_rate = await generate_chunked(tts_model, text, voice_prompt, **gen_kwargs)
+
+        # --- Normalize (generate and regenerate always; retry skips) -----
+        if normalize or mode == "regenerate":
+            audio = normalize_audio(audio)
+
+        duration = len(audio) / sample_rate
+
+        # --- Persist audio and update status -----------------------------
+        if mode == "generate":
+            final_path = _save_generate(
+                generation_id=generation_id,
+                audio=audio,
+                sample_rate=sample_rate,
+                effects_chain=effects_chain,
+                save_audio=save_audio,
+                db=bg_db,
+            )
+        elif mode == "retry":
+            final_path = _save_retry(
+                generation_id=generation_id,
+                audio=audio,
+                sample_rate=sample_rate,
+                save_audio=save_audio,
+            )
+        elif mode == "regenerate":
+            final_path = _save_regenerate(
+                generation_id=generation_id,
+                version_id=version_id,
+                audio=audio,
+                sample_rate=sample_rate,
+                save_audio=save_audio,
+                db=bg_db,
+            )
+
+        await history.update_generation_status(
+            generation_id=generation_id,
+            status="completed",
+            db=bg_db,
+            audio_path=final_path,
+            duration=duration,
+        )
+
+    except asyncio.CancelledError:
+        await history.update_generation_status(
+            generation_id=generation_id,
+            status="failed",
+            db=bg_db,
+            error="Generation cancelled",
+        )
+    except Exception as e:
+        traceback.print_exc()
+        await history.update_generation_status(
+            generation_id=generation_id,
+            status="failed",
+            db=bg_db,
+            error=str(e),
+        )
+    finally:
+        task_manager.complete_generation(generation_id)
+        bg_db.close()
+
+
+def _save_generate(
+    *,
+    generation_id: str,
+    audio,
+    sample_rate: int,
+    effects_chain: Optional[list],
+    save_audio,
+    db,
+) -> str:
+    """Save clean version and optionally an effects-processed version.
+
+    Returns the final audio path (processed if effects were applied,
+    otherwise clean).
+    """
+    from . import versions as versions_mod
+
+    clean_audio_path = config.get_generations_dir() / f"{generation_id}.wav"
+    save_audio(audio, str(clean_audio_path), sample_rate)
+
+    has_effects = effects_chain and any(e.get("enabled", True) for e in effects_chain)
+
+    versions_mod.create_version(
+        generation_id=generation_id,
+        label="original",
+        audio_path=config.to_storage_path(clean_audio_path),
+        db=db,
+        effects_chain=None,
+        is_default=not has_effects,
+    )
+
+    final_audio_path = str(clean_audio_path)
+
+    if has_effects:
+        from ..utils.effects import apply_effects, validate_effects_chain
+
+        assert effects_chain is not None
+
+        error_msg = validate_effects_chain(effects_chain)
+        if error_msg:
+            import logging
+            logging.getLogger(__name__).warning("invalid effects chain, skipping: %s", error_msg)
+            versions_mod.set_default_version(
+                versions_mod.list_versions(generation_id, db)[0].id, db
+            )
+        else:
+            processed_audio = apply_effects(audio, sample_rate, effects_chain)
+            processed_path = config.get_generations_dir() / f"{generation_id}_processed.wav"
+            save_audio(processed_audio, str(processed_path), sample_rate)
+            final_audio_path = str(processed_path)
+            versions_mod.create_version(
+                generation_id=generation_id,
+                label="version-2",
+                audio_path=config.to_storage_path(processed_path),
+                db=db,
+                effects_chain=effects_chain,
+                is_default=True,
+            )
+
+    return config.to_storage_path(final_audio_path)
+
+
+def _save_retry(
+    *,
+    generation_id: str,
+    audio,
+    sample_rate: int,
+    save_audio,
+) -> str:
+    """Save retry output -- single file, no versions.
+
+    Returns the audio path.
+    """
+    audio_path = config.get_generations_dir() / f"{generation_id}.wav"
+    save_audio(audio, str(audio_path), sample_rate)
+    return config.to_storage_path(audio_path)
+
+
+def _save_regenerate(
+    *,
+    generation_id: str,
+    version_id: Optional[str],
+    audio,
+    sample_rate: int,
+    save_audio,
+    db,
+) -> str:
+    """Save regeneration output as a new version with auto-label.
+
+    Returns the audio path.
+    """
+    from . import versions as versions_mod
+
+    import uuid as _uuid
+
+    suffix = _uuid.uuid4().hex[:8]
+    audio_path = config.get_generations_dir() / f"{generation_id}_{suffix}.wav"
+    save_audio(audio, str(audio_path), sample_rate)
+
+    # Count via DB query rather than list length to avoid TOCTOU race
+    from ..database import GenerationVersion as DBGenerationVersion
+
+    count = db.query(DBGenerationVersion).filter_by(generation_id=generation_id).count()
+    label = f"take-{count + 1}"
+
+    versions_mod.create_version(
+        generation_id=generation_id,
+        label=label,
+        audio_path=config.to_storage_path(audio_path),
+        db=db,
+        effects_chain=None,
+        is_default=True,
+    )
+
+    return config.to_storage_path(audio_path)

backend/services/history.py

@@ -0,0 +1,368 @@
+"""
+Generation history management module.
+"""
+
+from typing import List, Optional, Tuple
+from datetime import datetime
+import uuid
+import shutil
+from pathlib import Path
+from sqlalchemy.orm import Session
+from sqlalchemy import or_
+
+from ..models import GenerationRequest, GenerationResponse, HistoryQuery, HistoryResponse, HistoryListResponse, GenerationVersionResponse, EffectConfig
+from ..database import Generation as DBGeneration, GenerationVersion as DBGenerationVersion, VoiceProfile as DBVoiceProfile
+from .. import config
+
+
+def _get_versions_for_generation(generation_id: str, db: Session) -> tuple:
+    """Get versions list and active version ID for a generation."""
+    import json
+    versions_rows = (
+        db.query(DBGenerationVersion)
+        .filter_by(generation_id=generation_id)
+        .order_by(DBGenerationVersion.created_at)
+        .all()
+    )
+    if not versions_rows:
+        return None, None
+
+    versions = []
+    active_version_id = None
+    for v in versions_rows:
+        effects_chain = None
+        if v.effects_chain:
+            try:
+                raw = json.loads(v.effects_chain)
+                effects_chain = [EffectConfig(**e) for e in raw]
+            except Exception:
+                pass
+        versions.append(GenerationVersionResponse(
+            id=v.id,
+            generation_id=v.generation_id,
+            label=v.label,
+            audio_path=v.audio_path,
+            effects_chain=effects_chain,
+            is_default=v.is_default,
+            created_at=v.created_at,
+        ))
+        if v.is_default:
+            active_version_id = v.id
+
+    return versions, active_version_id
+
+
+async def create_generation(
+    profile_id: str,
+    text: str,
+    language: str,
+    audio_path: str,
+    duration: float,
+    seed: Optional[int],
+    db: Session,
+    instruct: Optional[str] = None,
+    generation_id: Optional[str] = None,
+    status: str = "completed",
+    engine: Optional[str] = "qwen",
+    model_size: Optional[str] = None,
+) -> GenerationResponse:
+    """
+    Create a new generation history entry.
+
+    Args:
+        profile_id: Profile ID used for generation
+        text: Generated text
+        language: Language code
+        audio_path: Path where audio was saved
+        duration: Audio duration in seconds
+        seed: Random seed used (if any)
+        db: Database session
+        instruct: Natural language instruction used (if any)
+        generation_id: Pre-assigned ID (for async generation flow)
+        status: Generation status (generating, completed, failed)
+        engine: TTS engine used (qwen, luxtts, chatterbox, chatterbox_turbo)
+        model_size: Model size variant (1.7B, 0.6B) — only relevant for qwen
+
+    Returns:
+        Created generation entry
+    """
+    db_generation = DBGeneration(
+        id=generation_id or str(uuid.uuid4()),
+        profile_id=profile_id,
+        text=text,
+        language=language,
+        audio_path=audio_path,
+        duration=duration,
+        seed=seed,
+        instruct=instruct,
+        engine=engine,
+        model_size=model_size,
+        status=status,
+        created_at=datetime.utcnow(),
+    )
+
+    db.add(db_generation)
+    db.commit()
+    db.refresh(db_generation)
+
+    return GenerationResponse.model_validate(db_generation)
+
+
+async def update_generation_status(
+    generation_id: str,
+    status: str,
+    db: Session,
+    audio_path: Optional[str] = None,
+    duration: Optional[float] = None,
+    error: Optional[str] = None,
+) -> Optional[GenerationResponse]:
+    """Update the status of a generation (used by async generation flow)."""
+    generation = db.query(DBGeneration).filter_by(id=generation_id).first()
+    if not generation:
+        return None
+
+    generation.status = status
+    if audio_path is not None:
+        generation.audio_path = audio_path
+    if duration is not None:
+        generation.duration = duration
+    if error is not None:
+        generation.error = error
+
+    db.commit()
+    db.refresh(generation)
+    return GenerationResponse.model_validate(generation)
+
+
+async def get_generation(
+    generation_id: str,
+    db: Session,
+) -> Optional[GenerationResponse]:
+    """
+    Get a generation by ID.
+    
+    Args:
+        generation_id: Generation ID
+        db: Database session
+        
+    Returns:
+        Generation or None if not found
+    """
+    generation = db.query(DBGeneration).filter_by(id=generation_id).first()
+    if not generation:
+        return None
+    
+    return GenerationResponse.model_validate(generation)
+
+
+async def list_generations(
+    query: HistoryQuery,
+    db: Session,
+) -> HistoryListResponse:
+    """
+    List generations with optional filters.
+    
+    Args:
+        query: Query parameters (filters, pagination)
+        db: Database session
+        
+    Returns:
+        HistoryListResponse with items and total count
+    """
+    # Build base query with join to get profile name
+    q = db.query(
+        DBGeneration,
+        DBVoiceProfile.name.label('profile_name')
+    ).join(
+        DBVoiceProfile,
+        DBGeneration.profile_id == DBVoiceProfile.id
+    )
+    
+    # Apply profile filter
+    if query.profile_id:
+        q = q.filter(DBGeneration.profile_id == query.profile_id)
+    
+    # Apply search filter (searches in text content)
+    if query.search:
+        search_pattern = f"%{query.search}%"
+        q = q.filter(DBGeneration.text.like(search_pattern))
+    
+    # Get total count before pagination
+    total_count = q.count()
+    
+    # Apply ordering (newest first)
+    q = q.order_by(DBGeneration.created_at.desc())
+    
+    # Apply pagination
+    q = q.offset(query.offset).limit(query.limit)
+    
+    # Execute query
+    results = q.all()
+    
+    # Convert to HistoryResponse with profile_name
+    items = []
+    for generation, profile_name in results:
+        versions, active_version_id = _get_versions_for_generation(generation.id, db)
+        items.append(HistoryResponse(
+            id=generation.id,
+            profile_id=generation.profile_id,
+            profile_name=profile_name,
+            text=generation.text,
+            language=generation.language,
+            audio_path=generation.audio_path,
+            duration=generation.duration,
+            seed=generation.seed,
+            instruct=generation.instruct,
+            engine=generation.engine or "qwen",
+            model_size=generation.model_size,
+            status=generation.status or "completed",
+            error=generation.error,
+            is_favorited=bool(generation.is_favorited),
+            created_at=generation.created_at,
+            versions=versions,
+            active_version_id=active_version_id,
+        ))
+    
+    return HistoryListResponse(
+        items=items,
+        total=total_count,
+    )
+
+
+async def delete_generation(
+    generation_id: str,
+    db: Session,
+) -> bool:
+    """
+    Delete a generation.
+    
+    Args:
+        generation_id: Generation ID
+        db: Database session
+        
+    Returns:
+        True if deleted, False if not found
+    """
+    generation = db.query(DBGeneration).filter_by(id=generation_id).first()
+    if not generation:
+        return False
+
+    # Delete all version files and records
+    from . import versions as versions_mod
+    versions_mod.delete_versions_for_generation(generation_id, db)
+
+    # Delete main audio file (if not already removed by version cleanup)
+    if generation.audio_path:
+        audio_path = config.resolve_storage_path(generation.audio_path)
+        if audio_path is not None and audio_path.exists():
+            audio_path.unlink()
+
+    # Delete from database
+    db.delete(generation)
+    db.commit()
+    
+    return True
+
+
+async def delete_failed_generations(db: Session) -> int:
+    """
+    Delete every generation whose status is 'failed'.
+
+    Used by the "Clear failed" action in the UI so users can tidy up
+    history after the model wasn't loaded, the app was closed mid-run,
+    or a generation otherwise errored out (see issue #410).
+
+    Returns:
+        Number of generations deleted.
+    """
+    from . import versions as versions_mod
+
+    failed = db.query(DBGeneration).filter(DBGeneration.status == "failed").all()
+    count = 0
+    for generation in failed:
+        # Clean up version files/rows first.
+        versions_mod.delete_versions_for_generation(generation.id, db)
+
+        # Remove the main audio file if it somehow made it to disk.
+        if generation.audio_path:
+            audio_path = config.resolve_storage_path(generation.audio_path)
+            if audio_path is not None and audio_path.exists():
+                try:
+                    audio_path.unlink()
+                except OSError:
+                    # Best-effort cleanup — don't abort the whole sweep
+                    # if a single file can't be removed.
+                    pass
+
+        db.delete(generation)
+        count += 1
+
+    db.commit()
+    return count
+
+
+async def delete_generations_by_profile(
+    profile_id: str,
+    db: Session,
+) -> int:
+    """
+    Delete all generations for a profile.
+    
+    Args:
+        profile_id: Profile ID
+        db: Database session
+        
+    Returns:
+        Number of generations deleted
+    """
+    generations = db.query(DBGeneration).filter_by(profile_id=profile_id).all()
+    
+    count = 0
+    for generation in generations:
+        # Delete associated version files and rows first
+        from . import versions as versions_mod
+        versions_mod.delete_versions_for_generation(generation.id, db)
+
+        # Delete audio file
+        audio_path = config.resolve_storage_path(generation.audio_path)
+        if audio_path is not None and audio_path.exists():
+            audio_path.unlink()
+        
+        # Delete from database
+        db.delete(generation)
+        count += 1
+    
+    db.commit()
+    
+    return count
+
+
+async def get_generation_stats(db: Session) -> dict:
+    """
+    Get generation statistics.
+    
+    Args:
+        db: Database session
+        
+    Returns:
+        Statistics dictionary
+    """
+    from sqlalchemy import func
+    
+    total = db.query(func.count(DBGeneration.id)).scalar()
+    
+    total_duration = db.query(func.sum(DBGeneration.duration)).scalar() or 0
+    
+    # Get generations by profile
+    by_profile = db.query(
+        DBGeneration.profile_id,
+        func.count(DBGeneration.id).label('count')
+    ).group_by(DBGeneration.profile_id).all()
+    
+    return {
+        "total_generations": total,
+        "total_duration_seconds": total_duration,
+        "generations_by_profile": {
+            profile_id: count for profile_id, count in by_profile
+        },
+    }

backend/services/profiles.py

@@ -0,0 +1,686 @@
+"""Voice profile management module."""
+
+import json as _json
+import logging
+import shutil
+import uuid
+from datetime import datetime
+from pathlib import Path
+
+from sqlalchemy import func
+from sqlalchemy.orm import Session
+
+from .. import config
+from ..database import Generation as DBGeneration, ProfileSample as DBProfileSample, VoiceProfile as DBVoiceProfile
+from ..models import (
+    EffectConfig,
+    ProfileSampleResponse,
+    VoiceProfileCreate,
+    VoiceProfileResponse,
+)
+from ..utils.audio import save_audio, validate_and_load_reference_audio
+from ..utils.cache import _get_cache_dir, clear_profile_cache
+from ..utils.images import process_avatar, validate_image
+
+logger = logging.getLogger(__name__)
+
+CLONING_ENGINES = {"qwen", "luxtts", "chatterbox", "chatterbox_turbo", "tada"}
+
+
+def _profile_to_response(
+    profile: DBVoiceProfile,
+    generation_count: int = 0,
+    sample_count: int = 0,
+) -> VoiceProfileResponse:
+    """Convert a DB profile to a VoiceProfileResponse, deserializing effects_chain."""
+    effects_chain = None
+    if profile.effects_chain:
+        try:
+            raw = _json.loads(profile.effects_chain)
+            effects_chain = [EffectConfig(**e) for e in raw]
+        except Exception as e:
+            import logging
+
+            logging.warning(f"Failed to parse effects_chain for profile {profile.id}: {e}")
+    return VoiceProfileResponse(
+        id=profile.id,
+        name=profile.name,
+        description=profile.description,
+        language=profile.language,
+        avatar_path=profile.avatar_path,
+        effects_chain=effects_chain,
+        voice_type=getattr(profile, "voice_type", None) or "cloned",
+        preset_engine=getattr(profile, "preset_engine", None),
+        preset_voice_id=getattr(profile, "preset_voice_id", None),
+        design_prompt=getattr(profile, "design_prompt", None),
+        default_engine=getattr(profile, "default_engine", None),
+        generation_count=generation_count,
+        sample_count=sample_count,
+        created_at=profile.created_at,
+        updated_at=profile.updated_at,
+    )
+
+
+def _get_preset_voice_ids(engine: str) -> set[str]:
+    if engine == "kokoro":
+        from ..backends.kokoro_backend import KOKORO_VOICES
+
+        return {voice_id for voice_id, _name, _gender, _lang in KOKORO_VOICES}
+
+    if engine == "qwen_custom_voice":
+        from ..backends.qwen_custom_voice_backend import QWEN_CUSTOM_VOICES
+
+        return {voice_id for voice_id, _name, _gender, _lang, _desc in QWEN_CUSTOM_VOICES}
+
+    return set()
+
+
+def _validate_profile_fields(
+    *,
+    voice_type: str,
+    preset_engine: str | None,
+    preset_voice_id: str | None,
+    design_prompt: str | None,
+    default_engine: str | None,
+) -> str | None:
+    if voice_type == "preset":
+        if not preset_engine or not preset_voice_id:
+            return "Preset profiles require both preset_engine and preset_voice_id"
+        if default_engine and default_engine != preset_engine:
+            return "Preset profiles must use their preset_engine as default_engine"
+
+        available_voice_ids = _get_preset_voice_ids(preset_engine)
+        if available_voice_ids and preset_voice_id not in available_voice_ids:
+            return f"Preset voice '{preset_voice_id}' is not valid for engine '{preset_engine}'"
+        return None
+
+    if voice_type == "designed":
+        if not design_prompt or not design_prompt.strip():
+            return "Designed profiles require a design_prompt"
+        if preset_engine or preset_voice_id:
+            return "Designed profiles cannot set preset_engine or preset_voice_id"
+        return None
+
+    if preset_engine or preset_voice_id:
+        return "Cloned profiles cannot set preset_engine or preset_voice_id"
+    if design_prompt:
+        return "Cloned profiles cannot set design_prompt"
+    if default_engine and default_engine not in CLONING_ENGINES:
+        return f"Cloned profiles cannot use default engine '{default_engine}'"
+    return None
+
+
+def validate_profile_engine(profile, engine: str) -> None:
+    voice_type = getattr(profile, "voice_type", None) or "cloned"
+
+    if voice_type == "preset":
+        preset_engine = getattr(profile, "preset_engine", None)
+        preset_voice_id = getattr(profile, "preset_voice_id", None)
+        if not preset_engine or not preset_voice_id:
+            raise ValueError(f"Preset profile {profile.id} is missing preset engine metadata")
+        if preset_engine != engine:
+            raise ValueError(
+                f"Preset profile {profile.id} only supports engine '{preset_engine}', not '{engine}'"
+            )
+        return
+
+    if voice_type == "designed":
+        design_prompt = getattr(profile, "design_prompt", None)
+        if not design_prompt or not design_prompt.strip():
+            raise ValueError(f"Designed profile {profile.id} is missing design_prompt")
+        return
+
+    if engine not in CLONING_ENGINES:
+        raise ValueError(f"Engine '{engine}' does not support cloned voice profiles")
+
+
+async def create_profile(
+    data: VoiceProfileCreate,
+    db: Session,
+) -> VoiceProfileResponse:
+    """
+    Create a new voice profile.
+
+    Args:
+        data: Profile creation data
+        db: Database session
+
+    Returns:
+        Created profile
+
+    Raises:
+        ValueError: If a profile with the same name already exists
+    """
+    existing_profile = db.query(DBVoiceProfile).filter_by(name=data.name).first()
+    if existing_profile:
+        raise ValueError(f"A profile with the name '{data.name}' already exists. Please choose a different name.")
+
+    # Auto-set default_engine for preset profiles
+    default_engine = data.default_engine
+    voice_type = data.voice_type or "cloned"
+    if voice_type == "preset" and data.preset_engine and not default_engine:
+        default_engine = data.preset_engine
+
+    validation_error = _validate_profile_fields(
+        voice_type=voice_type,
+        preset_engine=data.preset_engine,
+        preset_voice_id=data.preset_voice_id,
+        design_prompt=data.design_prompt,
+        default_engine=default_engine,
+    )
+    if validation_error:
+        raise ValueError(validation_error)
+
+    db_profile = DBVoiceProfile(
+        id=str(uuid.uuid4()),
+        name=data.name,
+        description=data.description,
+        language=data.language,
+        voice_type=voice_type,
+        preset_engine=data.preset_engine,
+        preset_voice_id=data.preset_voice_id,
+        design_prompt=data.design_prompt,
+        default_engine=default_engine,
+        created_at=datetime.utcnow(),
+        updated_at=datetime.utcnow(),
+    )
+
+    db.add(db_profile)
+    db.commit()
+    db.refresh(db_profile)
+
+    profile_dir = config.get_profiles_dir() / db_profile.id
+    profile_dir.mkdir(parents=True, exist_ok=True)
+
+    return _profile_to_response(db_profile)
+
+
+async def add_profile_sample(
+    profile_id: str,
+    audio_path: str,
+    reference_text: str,
+    db: Session,
+) -> ProfileSampleResponse:
+    """
+    Add a sample to a voice profile.
+
+    Args:
+        profile_id: Profile ID
+        audio_path: Path to temporary audio file
+        reference_text: Transcript of audio
+        db: Database session
+
+    Returns:
+        Created sample
+    """
+    import asyncio
+
+    profile = db.query(DBVoiceProfile).filter_by(id=profile_id).first()
+    if not profile:
+        raise ValueError(f"Profile {profile_id} not found")
+
+    # Validate and load audio in a single pass, off the event loop
+    is_valid, error_msg, audio, sr = await asyncio.to_thread(
+        validate_and_load_reference_audio, audio_path
+    )
+    if not is_valid:
+        raise ValueError(f"Invalid reference audio: {error_msg}")
+
+    sample_id = str(uuid.uuid4())
+    profile_dir = config.get_profiles_dir() / profile_id
+    profile_dir.mkdir(parents=True, exist_ok=True)
+
+    dest_path = profile_dir / f"{sample_id}.wav"
+    await asyncio.to_thread(save_audio, audio, str(dest_path), sr)
+
+    db_sample = DBProfileSample(
+        id=sample_id,
+        profile_id=profile_id,
+        audio_path=config.to_storage_path(dest_path),
+        reference_text=reference_text,
+    )
+
+    db.add(db_sample)
+
+    profile.updated_at = datetime.utcnow()
+
+    db.commit()
+    db.refresh(db_sample)
+
+    # Invalidate combined audio cache for this profile
+    # Since a new sample was added, any cached combined audio is now stale
+    clear_profile_cache(profile_id)
+
+    return ProfileSampleResponse.model_validate(db_sample)
+
+
+async def get_profile(
+    profile_id: str,
+    db: Session,
+) -> VoiceProfileResponse | None:
+    """
+    Get a voice profile by ID.
+
+    Args:
+        profile_id: Profile ID
+        db: Database session
+
+    Returns:
+        Profile or None if not found
+    """
+    profile = db.query(DBVoiceProfile).filter_by(id=profile_id).first()
+    if not profile:
+        return None
+
+    return _profile_to_response(profile)
+
+
+async def get_profile_samples(
+    profile_id: str,
+    db: Session,
+) -> list[ProfileSampleResponse]:
+    """
+    Get all samples for a profile.
+
+    Args:
+        profile_id: Profile ID
+        db: Database session
+
+    Returns:
+        List of samples
+    """
+    samples = db.query(DBProfileSample).filter_by(profile_id=profile_id).all()
+    return [ProfileSampleResponse.model_validate(s) for s in samples]
+
+
+async def list_profiles(db: Session) -> list[VoiceProfileResponse]:
+    """
+    List all voice profiles with generation and sample counts.
+
+    Args:
+        db: Database session
+
+    Returns:
+        List of profiles
+    """
+    profiles = db.query(DBVoiceProfile).order_by(DBVoiceProfile.created_at.desc()).all()
+
+    if not profiles:
+        return []
+
+    # Batch-fetch generation counts
+    gen_counts_rows = (
+        db.query(DBGeneration.profile_id, func.count(DBGeneration.id)).group_by(DBGeneration.profile_id).all()
+    )
+    gen_counts = {row[0]: row[1] for row in gen_counts_rows}
+
+    # Batch-fetch sample counts
+    sample_counts_rows = (
+        db.query(DBProfileSample.profile_id, func.count(DBProfileSample.id)).group_by(DBProfileSample.profile_id).all()
+    )
+    sample_counts = {row[0]: row[1] for row in sample_counts_rows}
+
+    return [
+        _profile_to_response(
+            p,
+            generation_count=gen_counts.get(p.id, 0),
+            sample_count=sample_counts.get(p.id, 0),
+        )
+        for p in profiles
+    ]
+
+
+async def update_profile(
+    profile_id: str,
+    data: VoiceProfileCreate,
+    db: Session,
+) -> VoiceProfileResponse | None:
+    """
+    Update a voice profile.
+
+    Args:
+        profile_id: Profile ID
+        data: Updated profile data
+        db: Database session
+
+    Returns:
+        Updated profile or None if not found
+
+    Raises:
+        ValueError: If a profile with the same name already exists (different profile)
+    """
+    profile = db.query(DBVoiceProfile).filter_by(id=profile_id).first()
+    if not profile:
+        return None
+
+    if profile.name != data.name:
+        existing_profile = db.query(DBVoiceProfile).filter_by(name=data.name).first()
+        if existing_profile:
+            raise ValueError(f"A profile with the name '{data.name}' already exists. Please choose a different name.")
+
+    voice_type = getattr(profile, "voice_type", None) or "cloned"
+    preset_engine = getattr(profile, "preset_engine", None)
+    preset_voice_id = getattr(profile, "preset_voice_id", None)
+    design_prompt = getattr(profile, "design_prompt", None)
+    default_engine = data.default_engine if data.default_engine is not None else getattr(profile, "default_engine", None)
+
+    validation_error = _validate_profile_fields(
+        voice_type=voice_type,
+        preset_engine=preset_engine,
+        preset_voice_id=preset_voice_id,
+        design_prompt=design_prompt,
+        default_engine=default_engine,
+    )
+    if validation_error:
+        raise ValueError(validation_error)
+
+    profile.name = data.name
+    profile.description = data.description
+    profile.language = data.language
+    if data.default_engine is not None:
+        profile.default_engine = data.default_engine or None  # empty string → NULL
+    profile.updated_at = datetime.utcnow()
+
+    db.commit()
+    db.refresh(profile)
+
+    return _profile_to_response(profile)
+
+
+async def delete_profile(
+    profile_id: str,
+    db: Session,
+) -> bool:
+    """
+    Delete a voice profile and all associated data.
+
+    Args:
+        profile_id: Profile ID
+        db: Database session
+
+    Returns:
+        True if deleted, False if not found
+    """
+    profile = db.query(DBVoiceProfile).filter_by(id=profile_id).first()
+    if not profile:
+        return False
+
+    db.query(DBProfileSample).filter_by(profile_id=profile_id).delete()
+
+    db.delete(profile)
+    db.commit()
+
+    profile_dir = config.get_profiles_dir() / profile_id
+    if profile_dir.exists():
+        shutil.rmtree(profile_dir)
+
+    # Clean up combined audio cache files for this profile
+    clear_profile_cache(profile_id)
+
+    return True
+
+
+async def delete_profile_sample(
+    sample_id: str,
+    db: Session,
+) -> bool:
+    """
+    Delete a profile sample.
+
+    Args:
+        sample_id: Sample ID
+        db: Database session
+
+    Returns:
+        True if deleted, False if not found
+    """
+    sample = db.query(DBProfileSample).filter_by(id=sample_id).first()
+    if not sample:
+        return False
+
+    # Store profile_id before deleting
+    profile_id = sample.profile_id
+
+    audio_path = config.resolve_storage_path(sample.audio_path)
+    if audio_path is not None and audio_path.exists():
+        audio_path.unlink()
+
+    db.delete(sample)
+    db.commit()
+
+    # Invalidate combined audio cache for this profile
+    # Since the sample set changed, any cached combined audio is now stale
+    clear_profile_cache(profile_id)
+
+    return True
+
+
+async def update_profile_sample(
+    sample_id: str,
+    reference_text: str,
+    db: Session,
+) -> ProfileSampleResponse | None:
+    """
+    Update a profile sample's reference text.
+
+    Args:
+        sample_id: Sample ID
+        reference_text: Updated reference text
+        db: Database session
+
+    Returns:
+        Updated sample or None if not found
+    """
+    sample = db.query(DBProfileSample).filter_by(id=sample_id).first()
+    if not sample:
+        return None
+
+    # Store profile_id before updating
+    profile_id = sample.profile_id
+
+    sample.reference_text = reference_text
+    db.commit()
+    db.refresh(sample)
+
+    # Invalidate combined audio cache for this profile
+    # Since the reference text changed, cache keys and combined text are now stale
+    clear_profile_cache(profile_id)
+
+    return ProfileSampleResponse.model_validate(sample)
+
+
+async def create_voice_prompt_for_profile(
+    profile_id: str,
+    db: Session,
+    use_cache: bool = True,
+    engine: str = "qwen",
+) -> dict:
+    """
+    Create a voice prompt from a profile.
+
+    For cloned profiles: combines all audio samples into a voice prompt.
+    For preset profiles: returns the engine-specific preset voice reference.
+    For designed profiles: returns the text design prompt (future).
+
+    Args:
+        profile_id: Profile ID
+        db: Database session
+        use_cache: Whether to use cached prompts
+        engine: TTS engine to create prompt for
+
+    Returns:
+        Voice prompt dictionary
+    """
+    from ..backends import get_tts_backend_for_engine
+
+    profile = db.query(DBVoiceProfile).filter_by(id=profile_id).first()
+    if not profile:
+        raise ValueError(f"Profile not found: {profile_id}")
+
+    voice_type = getattr(profile, "voice_type", None) or "cloned"
+    validate_profile_engine(profile, engine)
+
+    # ── Preset profiles: return engine-specific voice reference ──
+    if voice_type == "preset":
+        if not profile.preset_engine or not profile.preset_voice_id:
+            raise ValueError(f"Preset profile {profile_id} is missing preset engine metadata")
+        if profile.preset_engine != engine:
+            raise ValueError(
+                f"Preset profile {profile_id} only supports engine '{profile.preset_engine}', not '{engine}'"
+            )
+        return {
+            "voice_type": "preset",
+            "preset_engine": profile.preset_engine,
+            "preset_voice_id": profile.preset_voice_id,
+        }
+
+    # ── Designed profiles: return text description (future) ──
+    if voice_type == "designed":
+        if not profile.design_prompt or not profile.design_prompt.strip():
+            raise ValueError(f"Designed profile {profile_id} is missing design_prompt")
+        return {
+            "voice_type": "designed",
+            "design_prompt": profile.design_prompt,
+        }
+
+    if engine not in CLONING_ENGINES:
+        raise ValueError(f"Engine '{engine}' does not support cloned voice profiles")
+
+    # ── Cloned profiles: create from audio samples ──
+    samples = db.query(DBProfileSample).filter_by(profile_id=profile_id).all()
+
+    if not samples:
+        raise ValueError(f"No samples found for profile {profile_id}")
+
+    tts_model = get_tts_backend_for_engine(engine)
+
+    if len(samples) == 1:
+        sample = samples[0]
+        sample_audio_path = config.resolve_storage_path(sample.audio_path)
+        if sample_audio_path is None:
+            raise ValueError(f"Sample audio not found for profile {profile_id}")
+        voice_prompt, _ = await tts_model.create_voice_prompt(
+            str(sample_audio_path),
+            sample.reference_text,
+            use_cache=use_cache,
+        )
+        return voice_prompt
+
+    audio_paths = []
+    for sample in samples:
+        sample_audio_path = config.resolve_storage_path(sample.audio_path)
+        if sample_audio_path is None:
+            raise ValueError(f"Sample audio not found for profile {profile_id}")
+        audio_paths.append(str(sample_audio_path))
+    reference_texts = [s.reference_text for s in samples]
+
+    combined_audio, combined_text = await tts_model.combine_voice_prompts(
+        audio_paths,
+        reference_texts,
+    )
+
+    # Save combined audio to cache directory (persistent)
+    # Create a hash of sample IDs to identify this specific combination
+    import hashlib
+
+    sample_ids_str = "-".join(sorted([s.id for s in samples]))
+    combination_hash = hashlib.md5(sample_ids_str.encode()).hexdigest()[:12]
+
+    cache_dir = _get_cache_dir()
+    cache_dir.mkdir(parents=True, exist_ok=True)
+    combined_path = cache_dir / f"combined_{profile_id}_{combination_hash}.wav"
+
+    save_audio(combined_audio, str(combined_path), 24000)
+
+    voice_prompt, _ = await tts_model.create_voice_prompt(
+        str(combined_path),
+        combined_text,
+        use_cache=use_cache,
+    )
+    return voice_prompt
+
+
+async def upload_avatar(
+    profile_id: str,
+    image_path: str,
+    db: Session,
+) -> VoiceProfileResponse:
+    """
+    Upload and process avatar image for a profile.
+
+    Args:
+        profile_id: Profile ID
+        image_path: Path to uploaded image file
+        db: Database session
+
+    Returns:
+        Updated profile
+    """
+    profile = db.query(DBVoiceProfile).filter_by(id=profile_id).first()
+    if not profile:
+        raise ValueError(f"Profile {profile_id} not found")
+
+    is_valid, error_msg = validate_image(image_path)
+    if not is_valid:
+        raise ValueError(error_msg)
+
+    if profile.avatar_path:
+        old_avatar = config.resolve_storage_path(profile.avatar_path)
+        if old_avatar is not None and old_avatar.exists():
+            old_avatar.unlink()
+
+    # Determine file extension from uploaded file
+    from PIL import Image
+
+    with Image.open(image_path) as img:
+        # Normalize JPEG variants (MPO is multi-picture format from some cameras)
+        img_format = img.format
+        if img_format in ("MPO", "JPG"):
+            img_format = "JPEG"
+
+        ext_map = {"PNG": ".png", "JPEG": ".jpg", "WEBP": ".webp"}
+        ext = ext_map.get(img_format, ".png")
+
+    profile_dir = config.get_profiles_dir() / profile_id
+    profile_dir.mkdir(parents=True, exist_ok=True)
+    output_path = profile_dir / f"avatar{ext}"
+
+    process_avatar(image_path, str(output_path))
+
+    profile.avatar_path = config.to_storage_path(output_path)
+    profile.updated_at = datetime.utcnow()
+
+    db.commit()
+    db.refresh(profile)
+
+    return _profile_to_response(profile)
+
+
+async def delete_avatar(
+    profile_id: str,
+    db: Session,
+) -> bool:
+    """
+    Delete avatar image for a profile.
+
+    Args:
+        profile_id: Profile ID
+        db: Database session
+
+    Returns:
+        True if deleted, False if not found or no avatar
+    """
+    profile = db.query(DBVoiceProfile).filter_by(id=profile_id).first()
+    if not profile or not profile.avatar_path:
+        return False
+
+    avatar_path = config.resolve_storage_path(profile.avatar_path)
+    if avatar_path is not None and avatar_path.exists():
+        avatar_path.unlink()
+
+    profile.avatar_path = None
+    profile.updated_at = datetime.utcnow()
+
+    db.commit()
+
+    return True

backend/services/stories.py

@@ -0,0 +1,925 @@
+"""
+Story management module.
+"""
+
+from typing import List, Optional
+from datetime import datetime
+import uuid
+import tempfile
+from pathlib import Path
+from sqlalchemy.orm import Session
+from sqlalchemy import func
+
+from .. import config
+from ..models import (
+    StoryCreate,
+    StoryResponse,
+    StoryDetailResponse,
+    StoryItemDetail,
+    StoryItemCreate,
+    StoryItemBatchUpdate,
+    StoryItemMove,
+    StoryItemTrim,
+    StoryItemSplit,
+    StoryItemVersionUpdate,
+)
+from ..database import (
+    Story as DBStory,
+    StoryItem as DBStoryItem,
+    Generation as DBGeneration,
+    VoiceProfile as DBVoiceProfile,
+)
+from .history import _get_versions_for_generation
+from ..utils.audio import load_audio, save_audio
+import numpy as np
+
+
+def _build_item_detail(
+    item: DBStoryItem,
+    generation: DBGeneration,
+    profile_name: str,
+    db: Session,
+) -> StoryItemDetail:
+    """Build a StoryItemDetail with version info from a story item and its generation."""
+    versions, active_version_id = _get_versions_for_generation(generation.id, db)
+
+    # Resolve the audio path: if version_id is set, use that version's audio
+    audio_path = generation.audio_path
+    if item.version_id and versions:
+        for v in versions:
+            if v.id == item.version_id:
+                audio_path = v.audio_path
+                break
+
+    return StoryItemDetail(
+        id=item.id,
+        story_id=item.story_id,
+        generation_id=item.generation_id,
+        version_id=getattr(item, "version_id", None),
+        start_time_ms=item.start_time_ms,
+        track=item.track,
+        trim_start_ms=getattr(item, "trim_start_ms", 0),
+        trim_end_ms=getattr(item, "trim_end_ms", 0),
+        created_at=item.created_at,
+        profile_id=generation.profile_id,
+        profile_name=profile_name,
+        text=generation.text,
+        language=generation.language,
+        audio_path=audio_path,
+        duration=generation.duration,
+        seed=generation.seed,
+        instruct=generation.instruct,
+        generation_created_at=generation.created_at,
+        versions=versions,
+        active_version_id=active_version_id,
+    )
+
+
+async def create_story(
+    data: StoryCreate,
+    db: Session,
+) -> StoryResponse:
+    """
+    Create a new story.
+
+    Args:
+        data: Story creation data
+        db: Database session
+
+    Returns:
+        Created story
+    """
+    db_story = DBStory(
+        id=str(uuid.uuid4()),
+        name=data.name,
+        description=data.description,
+        created_at=datetime.utcnow(),
+        updated_at=datetime.utcnow(),
+    )
+
+    db.add(db_story)
+    db.commit()
+    db.refresh(db_story)
+
+    item_count = db.query(func.count(DBStoryItem.id)).filter(DBStoryItem.story_id == db_story.id).scalar()
+
+    response = StoryResponse.model_validate(db_story)
+    response.item_count = item_count
+    return response
+
+
+async def list_stories(
+    db: Session,
+) -> List[StoryResponse]:
+    """
+    List all stories.
+
+    Args:
+        db: Database session
+
+    Returns:
+        List of stories with item counts
+    """
+    stories = db.query(DBStory).order_by(DBStory.updated_at.desc()).all()
+
+    result = []
+    for story in stories:
+        item_count = db.query(func.count(DBStoryItem.id)).filter(DBStoryItem.story_id == story.id).scalar()
+
+        response = StoryResponse.model_validate(story)
+        response.item_count = item_count
+        result.append(response)
+
+    return result
+
+
+async def get_story(
+    story_id: str,
+    db: Session,
+) -> Optional[StoryDetailResponse]:
+    """
+    Get a story with all its items.
+
+    Args:
+        story_id: Story ID
+        db: Database session
+
+    Returns:
+        Story with items or None if not found
+    """
+    story = db.query(DBStory).filter_by(id=story_id).first()
+    if not story:
+        return None
+
+    items = (
+        db.query(DBStoryItem, DBGeneration, DBVoiceProfile.name.label("profile_name"))
+        .join(DBGeneration, DBStoryItem.generation_id == DBGeneration.id)
+        .join(DBVoiceProfile, DBGeneration.profile_id == DBVoiceProfile.id)
+        .filter(DBStoryItem.story_id == story_id)
+        .order_by(DBStoryItem.start_time_ms)
+        .all()
+    )
+
+    item_details = []
+    for item, generation, profile_name in items:
+        item_details.append(_build_item_detail(item, generation, profile_name, db))
+
+    response = StoryDetailResponse.model_validate(story)
+    response.items = item_details
+    return response
+
+
+async def update_story(
+    story_id: str,
+    data: StoryCreate,
+    db: Session,
+) -> Optional[StoryResponse]:
+    """
+    Update a story.
+
+    Args:
+        story_id: Story ID
+        data: Update data
+        db: Database session
+
+    Returns:
+        Updated story or None if not found
+    """
+    story = db.query(DBStory).filter_by(id=story_id).first()
+    if not story:
+        return None
+
+    story.name = data.name
+    story.description = data.description
+    story.updated_at = datetime.utcnow()
+
+    db.commit()
+    db.refresh(story)
+
+    item_count = db.query(func.count(DBStoryItem.id)).filter(DBStoryItem.story_id == story.id).scalar()
+
+    response = StoryResponse.model_validate(story)
+    response.item_count = item_count
+    return response
+
+
+async def delete_story(
+    story_id: str,
+    db: Session,
+) -> bool:
+    """
+    Delete a story and all its items.
+
+    Args:
+        story_id: Story ID
+        db: Database session
+
+    Returns:
+        True if deleted, False if not found
+    """
+    story = db.query(DBStory).filter_by(id=story_id).first()
+    if not story:
+        return False
+
+    # Delete all items
+    db.query(DBStoryItem).filter_by(story_id=story_id).delete()
+
+    # Delete story
+    db.delete(story)
+    db.commit()
+
+    return True
+
+
+async def add_item_to_story(
+    story_id: str,
+    data: StoryItemCreate,
+    db: Session,
+) -> Optional[StoryItemDetail]:
+    """
+    Add a generation to a story.
+
+    Args:
+        story_id: Story ID
+        data: Item creation data
+        db: Database session
+
+    Returns:
+        Created item detail or None if story/generation not found
+    """
+    # Verify story exists
+    story = db.query(DBStory).filter_by(id=story_id).first()
+    if not story:
+        return None
+
+    # Verify generation exists
+    generation = db.query(DBGeneration).filter_by(id=data.generation_id).first()
+    if not generation:
+        return None
+
+    # Check if generation is already in story
+    existing = db.query(DBStoryItem).filter_by(story_id=story_id, generation_id=data.generation_id).first()
+    if existing:
+        # Return existing item
+        profile = db.query(DBVoiceProfile).filter_by(id=generation.profile_id).first()
+        return _build_item_detail(existing, generation, profile.name if profile else "Unknown", db)
+
+    # Get track from data or default to 0
+    track = data.track if data.track is not None else 0
+
+    # Calculate start_time_ms if not provided
+    if data.start_time_ms is not None:
+        start_time_ms = data.start_time_ms
+    else:
+        existing_items = (
+            db.query(DBStoryItem, DBGeneration)
+            .join(DBGeneration, DBStoryItem.generation_id == DBGeneration.id)
+            .filter(
+                DBStoryItem.story_id == story_id,
+                DBStoryItem.track == track,
+            )
+            .all()
+        )
+
+        if not existing_items:
+            start_time_ms = 0
+        else:
+            max_end_time_ms = 0
+            for item, gen in existing_items:
+                item_end_ms = item.start_time_ms + int(gen.duration * 1000)
+                max_end_time_ms = max(max_end_time_ms, item_end_ms)
+
+            # Add 200ms gap after the last item
+            start_time_ms = max_end_time_ms + 200
+
+    # Create item
+    item = DBStoryItem(
+        id=str(uuid.uuid4()),
+        story_id=story_id,
+        generation_id=data.generation_id,
+        start_time_ms=start_time_ms,
+        track=track,
+        created_at=datetime.utcnow(),
+    )
+
+    db.add(item)
+
+    # Update story updated_at
+    story.updated_at = datetime.utcnow()
+
+    db.commit()
+    db.refresh(item)
+
+    # Get profile name
+    profile = db.query(DBVoiceProfile).filter_by(id=generation.profile_id).first()
+
+    return _build_item_detail(item, generation, profile.name if profile else "Unknown", db)
+
+
+async def move_story_item(
+    story_id: str,
+    item_id: str,
+    data: StoryItemMove,
+    db: Session,
+) -> Optional[StoryItemDetail]:
+    """
+    Move a story item (update position and/or track).
+
+    Args:
+        story_id: Story ID
+        item_id: Story item ID
+        data: New position and track data
+        db: Database session
+
+    Returns:
+        Updated item detail or None if not found
+    """
+    # Get the item
+    item = (
+        db.query(DBStoryItem)
+        .filter_by(
+            id=item_id,
+            story_id=story_id,
+        )
+        .first()
+    )
+    if not item:
+        return None
+
+    # Get the generation
+    generation = db.query(DBGeneration).filter_by(id=item.generation_id).first()
+    if not generation:
+        return None
+
+    # Update position and track
+    item.start_time_ms = data.start_time_ms
+    item.track = data.track
+
+    # Update story updated_at
+    story = db.query(DBStory).filter_by(id=story_id).first()
+    if story:
+        story.updated_at = datetime.utcnow()
+
+    db.commit()
+    db.refresh(item)
+
+    # Get profile name
+    profile = db.query(DBVoiceProfile).filter_by(id=generation.profile_id).first()
+
+    return _build_item_detail(item, generation, profile.name if profile else "Unknown", db)
+
+
+async def remove_item_from_story(
+    story_id: str,
+    item_id: str,
+    db: Session,
+) -> bool:
+    """
+    Remove a story item from a story.
+
+    Args:
+        story_id: Story ID
+        item_id: Story item ID to remove
+        db: Database session
+
+    Returns:
+        True if removed, False if not found
+    """
+    item = (
+        db.query(DBStoryItem)
+        .filter_by(
+            id=item_id,
+            story_id=story_id,
+        )
+        .first()
+    )
+    if not item:
+        return False
+
+    # Delete item
+    db.delete(item)
+
+    # Update story updated_at
+    story = db.query(DBStory).filter_by(id=story_id).first()
+    if story:
+        story.updated_at = datetime.utcnow()
+
+    db.commit()
+    return True
+
+
+async def trim_story_item(
+    story_id: str,
+    item_id: str,
+    data: StoryItemTrim,
+    db: Session,
+) -> Optional[StoryItemDetail]:
+    """
+    Trim a story item (update trim_start_ms and trim_end_ms).
+
+    Args:
+        story_id: Story ID
+        item_id: Story item ID
+        data: Trim data (trim_start_ms, trim_end_ms)
+        db: Database session
+
+    Returns:
+        Updated item detail or None if not found
+    """
+    # Get the item
+    item = (
+        db.query(DBStoryItem)
+        .filter_by(
+            id=item_id,
+            story_id=story_id,
+        )
+        .first()
+    )
+    if not item:
+        return None
+
+    # Get the generation
+    generation = db.query(DBGeneration).filter_by(id=item.generation_id).first()
+    if not generation:
+        return None
+
+    # Validate trim values don't exceed duration
+    max_duration_ms = int(generation.duration * 1000)
+    if data.trim_start_ms + data.trim_end_ms >= max_duration_ms:
+        return None  # Invalid trim - would result in zero or negative duration
+
+    # Update trim values
+    item.trim_start_ms = data.trim_start_ms
+    item.trim_end_ms = data.trim_end_ms
+
+    # Update story updated_at
+    story = db.query(DBStory).filter_by(id=story_id).first()
+    if story:
+        story.updated_at = datetime.utcnow()
+
+    db.commit()
+    db.refresh(item)
+
+    # Get profile name
+    profile = db.query(DBVoiceProfile).filter_by(id=generation.profile_id).first()
+
+    return _build_item_detail(item, generation, profile.name if profile else "Unknown", db)
+
+
+async def split_story_item(
+    story_id: str,
+    item_id: str,
+    data: StoryItemSplit,
+    db: Session,
+) -> Optional[List[StoryItemDetail]]:
+    """
+    Split a story item at a given time, creating two clips.
+
+    Args:
+        story_id: Story ID
+        item_id: Story item ID to split
+        data: Split data (split_time_ms - time within clip to split at)
+        db: Database session
+
+    Returns:
+        List of two updated item details (original and new) or None if not found/invalid
+    """
+    # Get the item with a row lock to prevent concurrent splits on the
+    # same clip (e.g. from rapid double-clicks racing each other).
+    item = (
+        db.query(DBStoryItem)
+        .filter_by(
+            id=item_id,
+            story_id=story_id,
+        )
+        .with_for_update()
+        .first()
+    )
+    if not item:
+        return None
+
+    # Get the generation
+    generation = db.query(DBGeneration).filter_by(id=item.generation_id).first()
+    if not generation:
+        return None
+
+    # Calculate effective duration and validate split point
+    current_trim_start = getattr(item, "trim_start_ms", 0)
+    current_trim_end = getattr(item, "trim_end_ms", 0)
+    original_duration_ms = int(generation.duration * 1000)
+    effective_duration_ms = original_duration_ms - current_trim_start - current_trim_end
+
+    # Validate split_time_ms is within the effective duration
+    if data.split_time_ms <= 0 or data.split_time_ms >= effective_duration_ms:
+        return None  # Invalid split point
+
+    # Calculate the absolute time in the original audio where we're splitting
+    absolute_split_ms = current_trim_start + data.split_time_ms
+
+    # Update original clip: trim from the end
+    item.trim_end_ms = original_duration_ms - absolute_split_ms
+
+    # Create new clip: starts after the split, trimmed from the start
+    new_item = DBStoryItem(
+        id=str(uuid.uuid4()),
+        story_id=story_id,
+        generation_id=item.generation_id,  # Same generation, different trim
+        version_id=getattr(item, "version_id", None),  # Preserve pinned version
+        start_time_ms=item.start_time_ms + data.split_time_ms,
+        track=item.track,
+        trim_start_ms=absolute_split_ms,
+        trim_end_ms=current_trim_end,
+        created_at=datetime.utcnow(),
+    )
+
+    db.add(new_item)
+
+    # Update story updated_at
+    story = db.query(DBStory).filter_by(id=story_id).first()
+    if story:
+        story.updated_at = datetime.utcnow()
+
+    db.commit()
+    db.refresh(item)
+    db.refresh(new_item)
+
+    # Get profile name
+    profile = db.query(DBVoiceProfile).filter_by(id=generation.profile_id).first()
+    profile_name = profile.name if profile else "Unknown"
+
+    return [
+        _build_item_detail(item, generation, profile_name, db),
+        _build_item_detail(new_item, generation, profile_name, db),
+    ]
+
+
+async def duplicate_story_item(
+    story_id: str,
+    item_id: str,
+    db: Session,
+) -> Optional[StoryItemDetail]:
+    """
+    Duplicate a story item, creating a copy with all properties.
+
+    Args:
+        story_id: Story ID
+        item_id: Story item ID to duplicate
+        db: Database session
+
+    Returns:
+        New item detail or None if not found
+    """
+    # Get the original item
+    original_item = (
+        db.query(DBStoryItem)
+        .filter_by(
+            id=item_id,
+            story_id=story_id,
+        )
+        .first()
+    )
+    if not original_item:
+        return None
+
+    # Get the generation
+    generation = db.query(DBGeneration).filter_by(id=original_item.generation_id).first()
+    if not generation:
+        return None
+
+    # Calculate effective duration
+    current_trim_start = getattr(original_item, "trim_start_ms", 0)
+    current_trim_end = getattr(original_item, "trim_end_ms", 0)
+    original_duration_ms = int(generation.duration * 1000)
+    effective_duration_ms = original_duration_ms - current_trim_start - current_trim_end
+
+    # Create duplicate item - place it right after the original
+    new_item = DBStoryItem(
+        id=str(uuid.uuid4()),
+        story_id=story_id,
+        generation_id=original_item.generation_id,  # Same generation as original
+        version_id=getattr(original_item, "version_id", None),  # Preserve pinned version
+        start_time_ms=original_item.start_time_ms + effective_duration_ms + 200,  # 200ms gap
+        track=original_item.track,
+        trim_start_ms=current_trim_start,
+        trim_end_ms=current_trim_end,
+        created_at=datetime.utcnow(),
+    )
+
+    db.add(new_item)
+
+    # Update story updated_at
+    story = db.query(DBStory).filter_by(id=story_id).first()
+    if story:
+        story.updated_at = datetime.utcnow()
+
+    db.commit()
+    db.refresh(new_item)
+
+    # Get profile name
+    profile = db.query(DBVoiceProfile).filter_by(id=generation.profile_id).first()
+
+    return _build_item_detail(new_item, generation, profile.name if profile else "Unknown", db)
+
+
+async def update_story_item_times(
+    story_id: str,
+    data: StoryItemBatchUpdate,
+    db: Session,
+) -> bool:
+    """
+    Update story item timecodes.
+
+    Args:
+        story_id: Story ID
+        data: Batch update data with timecodes
+        db: Database session
+
+    Returns:
+        True if updated, False if story not found or invalid
+    """
+    story = db.query(DBStory).filter_by(id=story_id).first()
+    if not story:
+        return False
+
+    # Get all items for this story
+    items = db.query(DBStoryItem).filter_by(story_id=story_id).all()
+    item_map = {item.generation_id: item for item in items}
+
+    # Verify all generation IDs belong to this story and update timecodes
+    for update in data.updates:
+        if update.generation_id not in item_map:
+            return False
+        item_map[update.generation_id].start_time_ms = update.start_time_ms
+
+    # Update story updated_at
+    story.updated_at = datetime.utcnow()
+
+    db.commit()
+    return True
+
+
+async def reorder_story_items(
+    story_id: str,
+    generation_ids: List[str],
+    db: Session,
+    gap_ms: int = 200,
+) -> Optional[List[StoryItemDetail]]:
+    """
+    Reorder story items and recalculate timecodes.
+
+    Args:
+        story_id: Story ID
+        generation_ids: List of generation IDs in the desired order
+        db: Database session
+        gap_ms: Gap in milliseconds between items (default 200ms)
+
+    Returns:
+        Updated list of story items with new timecodes, or None if invalid
+    """
+    story = db.query(DBStory).filter_by(id=story_id).first()
+    if not story:
+        return None
+
+    # Get all items for this story with their generation data
+    items_with_gen = (
+        db.query(DBStoryItem, DBGeneration, DBVoiceProfile.name.label("profile_name"))
+        .join(DBGeneration, DBStoryItem.generation_id == DBGeneration.id)
+        .join(DBVoiceProfile, DBGeneration.profile_id == DBVoiceProfile.id)
+        .filter(DBStoryItem.story_id == story_id)
+        .all()
+    )
+
+    # Create maps for quick lookup
+    item_map = {item.generation_id: (item, gen, profile_name) for item, gen, profile_name in items_with_gen}
+
+    # Verify all generation IDs belong to this story
+    if set(generation_ids) != set(item_map.keys()):
+        return None
+
+    # Recalculate timecodes based on new order
+    current_time_ms = 0
+    updated_items = []
+
+    for gen_id in generation_ids:
+        item, generation, profile_name = item_map[gen_id]
+
+        # Update the item's start time
+        item.start_time_ms = current_time_ms
+
+        # Calculate the duration in ms
+        duration_ms = int(generation.duration * 1000)
+
+        # Move to next position (current end + gap)
+        current_time_ms += duration_ms + gap_ms
+
+        # Build the response item
+        updated_items.append(_build_item_detail(item, generation, profile_name, db))
+
+    # Update story updated_at
+    story.updated_at = datetime.utcnow()
+
+    db.commit()
+    return updated_items
+
+
+async def set_story_item_version(
+    story_id: str,
+    item_id: str,
+    data: StoryItemVersionUpdate,
+    db: Session,
+) -> Optional[StoryItemDetail]:
+    """
+    Pin a story item to a specific generation version.
+
+    Args:
+        story_id: Story ID
+        item_id: Story item ID
+        data: Version update data (version_id or null for default)
+        db: Database session
+
+    Returns:
+        Updated item detail or None if not found
+    """
+    item = (
+        db.query(DBStoryItem)
+        .filter_by(
+            id=item_id,
+            story_id=story_id,
+        )
+        .first()
+    )
+    if not item:
+        return None
+
+    generation = db.query(DBGeneration).filter_by(id=item.generation_id).first()
+    if not generation:
+        return None
+
+    # Validate version_id belongs to this generation if provided
+    if data.version_id:
+        from ..database import GenerationVersion as DBGenerationVersion
+
+        version = (
+            db.query(DBGenerationVersion)
+            .filter_by(
+                id=data.version_id,
+                generation_id=item.generation_id,
+            )
+            .first()
+        )
+        if not version:
+            return None
+
+    item.version_id = data.version_id
+
+    # Update story updated_at
+    story = db.query(DBStory).filter_by(id=story_id).first()
+    if story:
+        story.updated_at = datetime.utcnow()
+
+    db.commit()
+    db.refresh(item)
+
+    profile = db.query(DBVoiceProfile).filter_by(id=generation.profile_id).first()
+
+    return _build_item_detail(item, generation, profile.name if profile else "Unknown", db)
+
+
+async def export_story_audio(
+    story_id: str,
+    db: Session,
+) -> Optional[bytes]:
+    """
+    Export story as single mixed audio file with timecode-based mixing.
+
+    Args:
+        story_id: Story ID
+        db: Database session
+
+    Returns:
+        Audio file bytes or None if story not found
+    """
+    story = db.query(DBStory).filter_by(id=story_id).first()
+    if not story:
+        return None
+
+    # Get all items ordered by start_time_ms
+    items = (
+        db.query(DBStoryItem, DBGeneration)
+        .join(DBGeneration, DBStoryItem.generation_id == DBGeneration.id)
+        .filter(DBStoryItem.story_id == story_id)
+        .order_by(DBStoryItem.start_time_ms)
+        .all()
+    )
+
+    if not items:
+        return None
+
+    # Load all audio files and calculate total duration
+    audio_data = []
+    sample_rate = 24000  # Default sample rate
+
+    for item, generation in items:
+        # Resolve audio path: use pinned version if set, otherwise generation default
+        resolved_audio_path = generation.audio_path
+        if getattr(item, "version_id", None):
+            from ..database import GenerationVersion as DBGenerationVersion
+
+            version = db.query(DBGenerationVersion).filter_by(id=item.version_id).first()
+            if version:
+                resolved_audio_path = version.audio_path
+
+        audio_path = config.resolve_storage_path(resolved_audio_path)
+        if audio_path is None or not audio_path.exists():
+            continue
+
+        try:
+            audio, sr = load_audio(str(audio_path), sample_rate=sample_rate)
+            sample_rate = sr  # Use actual sample rate from first file
+
+            # Get trim values
+            trim_start_ms = getattr(item, "trim_start_ms", 0)
+            trim_end_ms = getattr(item, "trim_end_ms", 0)
+
+            # Calculate effective duration
+            original_duration_ms = int(generation.duration * 1000)
+            effective_duration_ms = original_duration_ms - trim_start_ms - trim_end_ms
+
+            # Slice audio based on trim values
+            trim_start_sample = int((trim_start_ms / 1000.0) * sample_rate)
+            trim_end_sample = int((trim_end_ms / 1000.0) * sample_rate)
+
+            # Extract the trimmed portion
+            if trim_end_ms > 0:
+                trimmed_audio = (
+                    audio[trim_start_sample:-trim_end_sample] if trim_end_sample > 0 else audio[trim_start_sample:]
+                )
+            else:
+                trimmed_audio = audio[trim_start_sample:]
+
+            # Store audio with its timecode info
+            start_time_ms = item.start_time_ms
+
+            audio_data.append(
+                {
+                    "audio": trimmed_audio,
+                    "start_time_ms": start_time_ms,
+                    "duration_ms": effective_duration_ms,
+                }
+            )
+        except Exception:
+            # Skip files that can't be loaded
+            continue
+
+    if not audio_data:
+        return None
+
+    # Calculate total duration: max(start_time_ms + duration_ms)
+    max_end_time_ms = max((data["start_time_ms"] + data["duration_ms"] for data in audio_data), default=0)
+
+    # Convert to samples
+    total_samples = int((max_end_time_ms / 1000.0) * sample_rate)
+
+    # Create output buffer initialized to zeros
+    final_audio = np.zeros(total_samples, dtype=np.float32)
+
+    # Mix each audio segment at its timecode position
+    for data in audio_data:
+        audio = data["audio"]
+        start_time_ms = data["start_time_ms"]
+
+        # Calculate start sample index
+        start_sample = int((start_time_ms / 1000.0) * sample_rate)
+
+        # Ensure we don't exceed buffer bounds
+        audio_length = len(audio)
+        end_sample = min(start_sample + audio_length, total_samples)
+
+        if start_sample < total_samples:
+            # Trim audio if it extends beyond buffer
+            audio_to_mix = audio[: end_sample - start_sample]
+
+            # Mix: add audio to existing buffer (overlapping audio will sum)
+            # Normalize to prevent clipping (simple approach: divide by max)
+            final_audio[start_sample:end_sample] += audio_to_mix
+
+    # Normalize to prevent clipping
+    max_val = np.abs(final_audio).max()
+    if max_val > 1.0:
+        final_audio = final_audio / max_val
+
+    # Save to temporary file
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
+        tmp_path = tmp.name
+
+    try:
+        save_audio(final_audio, tmp_path, sample_rate)
+
+        # Read file bytes
+        with open(tmp_path, "rb") as f:
+            audio_bytes = f.read()
+
+        return audio_bytes
+    finally:
+        # Clean up temp file
+        Path(tmp_path).unlink(missing_ok=True)

backend/services/task_queue.py

@@ -0,0 +1,108 @@
+"""
+Serial generation queue — ensures only one TTS inference runs at a time
+to avoid GPU contention.
+"""
+
+import asyncio
+import traceback
+from dataclasses import dataclass
+from typing import Coroutine, Literal
+
+# Keep references to fire-and-forget background tasks to prevent GC
+_background_tasks: set = set()
+
+
+@dataclass
+class GenerationJob:
+    """Queued generation work plus the generation ID it belongs to."""
+
+    generation_id: str
+    coro: Coroutine
+
+
+# Generation queue — serializes TTS inference to avoid GPU contention
+_generation_queue: asyncio.Queue = None  # type: ignore  # initialized at startup
+_generation_worker_task: asyncio.Task | None = None
+_queued_generation_ids: set[str] = set()
+_running_generation_tasks: dict[str, asyncio.Task] = {}
+_cancelled_generation_ids: set[str] = set()
+
+
+def create_background_task(coro) -> asyncio.Task:
+    """Create a background task and prevent it from being garbage collected."""
+    task = asyncio.create_task(coro)
+    _background_tasks.add(task)
+    task.add_done_callback(_background_tasks.discard)
+    return task
+
+
+async def _generation_worker():
+    """Worker that processes generation tasks one at a time."""
+    while True:
+        job = await _generation_queue.get()
+        try:
+            if job.generation_id in _cancelled_generation_ids:
+                _cancelled_generation_ids.discard(job.generation_id)
+                job.coro.close()
+                continue
+
+            task = asyncio.create_task(job.coro)
+            _running_generation_tasks[job.generation_id] = task
+            _queued_generation_ids.discard(job.generation_id)
+            try:
+                await task
+            except asyncio.CancelledError:
+                if not task.cancelled():
+                    raise
+        except Exception:
+            traceback.print_exc()
+        finally:
+            _running_generation_tasks.pop(job.generation_id, None)
+            _queued_generation_ids.discard(job.generation_id)
+            _generation_queue.task_done()
+
+
+def enqueue_generation(generation_id: str, coro):
+    """Add a generation coroutine to the serial queue."""
+    if _generation_queue is None:
+        raise RuntimeError("Generation queue has not been initialized")
+
+    _queued_generation_ids.add(generation_id)
+    _generation_queue.put_nowait(GenerationJob(generation_id=generation_id, coro=coro))
+
+
+def cancel_generation(generation_id: str) -> Literal["queued", "running"] | None:
+    """Cancel a queued or running generation if it is still active."""
+    running_task = _running_generation_tasks.get(generation_id)
+    if running_task is not None:
+        running_task.cancel()
+        return "running"
+
+    if generation_id in _queued_generation_ids:
+        _queued_generation_ids.discard(generation_id)
+        _cancelled_generation_ids.add(generation_id)
+        return "queued"
+
+    return None
+
+
+def init_queue(force: bool = False):
+    """Initialize the generation queue and start the worker.
+
+    Must be called once during application startup (inside a running event loop).
+    """
+    global _generation_queue, _generation_worker_task
+    global _queued_generation_ids, _running_generation_tasks, _cancelled_generation_ids
+
+    if _generation_worker_task is not None and not _generation_worker_task.done():
+        if not force:
+            return
+        _generation_worker_task.cancel()
+        for task in list(_running_generation_tasks.values()):
+            task.cancel()
+
+    _generation_queue = asyncio.Queue()
+    _queued_generation_ids = set()
+    _running_generation_tasks = {}
+    _cancelled_generation_ids = set()
+    _generation_worker_task = create_background_task(_generation_worker())

backend/services/transcribe.py

@@ -0,0 +1,22 @@
+"""
+STT (Speech-to-Text) module - delegates to backend abstraction layer.
+"""
+
+from typing import Optional
+from ..backends import get_stt_backend, STTBackend
+
+
+def get_whisper_model() -> STTBackend:
+    """
+    Get STT backend instance (MLX or PyTorch based on platform).
+    
+    Returns:
+        STT backend instance
+    """
+    return get_stt_backend()
+
+
+def unload_whisper_model():
+    """Unload Whisper model to free memory."""
+    backend = get_stt_backend()
+    backend.unload_model()

backend/services/tts.py

@@ -0,0 +1,34 @@
+"""
+TTS inference module - delegates to backend abstraction layer.
+"""
+
+from typing import Optional
+import numpy as np
+import io
+import soundfile as sf
+
+from ..backends import get_tts_backend, TTSBackend
+
+
+def get_tts_model() -> TTSBackend:
+    """
+    Get TTS backend instance (MLX or PyTorch based on platform).
+    
+    Returns:
+        TTS backend instance
+    """
+    return get_tts_backend()
+
+
+def unload_tts_model():
+    """Unload TTS model to free memory."""
+    backend = get_tts_backend()
+    backend.unload_model()
+
+
+def audio_to_wav_bytes(audio: np.ndarray, sample_rate: int) -> bytes:
+    """Convert audio array to WAV bytes."""
+    buffer = io.BytesIO()
+    sf.write(buffer, audio, sample_rate, format="WAV")
+    buffer.seek(0)
+    return buffer.read()

backend/services/versions.py

@@ -0,0 +1,211 @@
+"""
+Generation versions management module.
+
+Each generation can have multiple audio versions: a clean (unprocessed)
+version and any number of processed versions with different effects chains.
+"""
+
+from __future__ import annotations
+
+import json
+import uuid
+from pathlib import Path
+from typing import List, Optional
+
+from sqlalchemy.orm import Session
+
+from ..database import (
+    GenerationVersion as DBGenerationVersion,
+    Generation as DBGeneration,
+)
+from ..models import GenerationVersionResponse, EffectConfig
+from .. import config
+
+
+def _version_response(v: DBGenerationVersion) -> GenerationVersionResponse:
+    """Convert a DB version row to a Pydantic response."""
+    effects_chain = None
+    if v.effects_chain:
+        raw = json.loads(v.effects_chain)
+        effects_chain = [EffectConfig(**e) for e in raw]
+    return GenerationVersionResponse(
+        id=v.id,
+        generation_id=v.generation_id,
+        label=v.label,
+        audio_path=v.audio_path,
+        effects_chain=effects_chain,
+        source_version_id=v.source_version_id,
+        is_default=v.is_default,
+        created_at=v.created_at,
+    )
+
+
+def list_versions(generation_id: str, db: Session) -> List[GenerationVersionResponse]:
+    """List all versions for a generation."""
+    versions = (
+        db.query(DBGenerationVersion)
+        .filter_by(generation_id=generation_id)
+        .order_by(DBGenerationVersion.created_at)
+        .all()
+    )
+    return [_version_response(v) for v in versions]
+
+
+def get_version(version_id: str, db: Session) -> Optional[GenerationVersionResponse]:
+    """Get a specific version by ID."""
+    v = db.query(DBGenerationVersion).filter_by(id=version_id).first()
+    if not v:
+        return None
+    return _version_response(v)
+
+
+def get_default_version(generation_id: str, db: Session) -> Optional[GenerationVersionResponse]:
+    """Get the default version for a generation."""
+    v = (
+        db.query(DBGenerationVersion)
+        .filter_by(generation_id=generation_id, is_default=True)
+        .first()
+    )
+    if not v:
+        # Fallback: return the first version
+        v = (
+            db.query(DBGenerationVersion)
+            .filter_by(generation_id=generation_id)
+            .order_by(DBGenerationVersion.created_at)
+            .first()
+        )
+    if not v:
+        return None
+    return _version_response(v)
+
+
+def create_version(
+    generation_id: str,
+    label: str,
+    audio_path: str,
+    db: Session,
+    effects_chain: Optional[List[dict]] = None,
+    is_default: bool = False,
+    source_version_id: Optional[str] = None,
+) -> GenerationVersionResponse:
+    """Create a new version for a generation.
+
+    If ``is_default`` is True, all other versions for this generation
+    are un-defaulted first.
+    """
+    if is_default:
+        _clear_defaults(generation_id, db)
+
+    version = DBGenerationVersion(
+        id=str(uuid.uuid4()),
+        generation_id=generation_id,
+        label=label,
+        audio_path=audio_path,
+        effects_chain=json.dumps(effects_chain) if effects_chain else None,
+        source_version_id=source_version_id,
+        is_default=is_default,
+    )
+    db.add(version)
+    db.commit()
+    db.refresh(version)
+
+    # If this version is the default, update the generation's audio_path
+    if is_default:
+        gen = db.query(DBGeneration).filter_by(id=generation_id).first()
+        if gen:
+            gen.audio_path = audio_path
+            db.commit()
+
+    return _version_response(version)
+
+
+def set_default_version(version_id: str, db: Session) -> Optional[GenerationVersionResponse]:
+    """Set a version as the default for its generation."""
+    version = db.query(DBGenerationVersion).filter_by(id=version_id).first()
+    if not version:
+        return None
+
+    _clear_defaults(version.generation_id, db)
+    version.is_default = True
+    db.commit()
+    db.refresh(version)
+
+    # Update generation's audio_path to point to this version
+    gen = db.query(DBGeneration).filter_by(id=version.generation_id).first()
+    if gen:
+        gen.audio_path = version.audio_path
+        db.commit()
+
+    return _version_response(version)
+
+
+def delete_version(version_id: str, db: Session) -> bool:
+    """Delete a version. Cannot delete the last remaining version."""
+    version = db.query(DBGenerationVersion).filter_by(id=version_id).first()
+    if not version:
+        return False
+
+    # Don't allow deleting the last version
+    count = (
+        db.query(DBGenerationVersion)
+        .filter_by(generation_id=version.generation_id)
+        .count()
+    )
+    if count <= 1:
+        return False
+
+    was_default = version.is_default
+    gen_id = version.generation_id
+
+    # Delete audio file
+    audio_path = config.resolve_storage_path(version.audio_path)
+    if audio_path is not None and audio_path.exists():
+        audio_path.unlink()
+
+    db.delete(version)
+    db.commit()
+
+    # If this was the default, promote the first remaining version
+    if was_default:
+        first = (
+            db.query(DBGenerationVersion)
+            .filter_by(generation_id=gen_id)
+            .order_by(DBGenerationVersion.created_at)
+            .first()
+        )
+        if first:
+            first.is_default = True
+            db.commit()
+            gen = db.query(DBGeneration).filter_by(id=gen_id).first()
+            if gen:
+                gen.audio_path = first.audio_path
+                db.commit()
+
+    return True
+
+
+def delete_versions_for_generation(generation_id: str, db: Session) -> int:
+    """Delete all versions for a generation (used when deleting a generation)."""
+    versions = (
+        db.query(DBGenerationVersion)
+        .filter_by(generation_id=generation_id)
+        .all()
+    )
+    count = 0
+    for v in versions:
+        audio_path = config.resolve_storage_path(v.audio_path)
+        if audio_path is not None and audio_path.exists():
+            audio_path.unlink()
+        db.delete(v)
+        count += 1
+    if count > 0:
+        db.commit()
+    return count
+
+
+def _clear_defaults(generation_id: str, db: Session) -> None:
+    """Clear the is_default flag on all versions for a generation."""
+    db.query(DBGenerationVersion).filter_by(
+        generation_id=generation_id, is_default=True
+    ).update({"is_default": False})
+    db.flush()

backend/tests/E2E_MODEL_TEST_DESIGN.md

@@ -0,0 +1,220 @@
+# End-to-End Model Generation Test — Design
+
+## Goal
+
+A single script, runnable on macOS and Windows, that exercises every TTS model against the **frozen PyInstaller binary** (not the dev server), captures per-model pass/fail and error messages, and exits non-zero if any model fails. Generation is strictly sequential — one model loaded at a time.
+
+## Test matrix (10 runs)
+
+Derived from `backend/backends/__init__.py:185-316`. Each row maps to one `POST /generate` call.
+
+| # | engine                | model_size | profile kind | notes |
+|---|-----------------------|------------|--------------|-------|
+| 1 | `qwen`                | `1.7B`     | cloned       | reference audio required |
+| 2 | `qwen`                | `0.6B`     | cloned       | |
+| 3 | `qwen_custom_voice`   | `1.7B`     | preset       | `preset_voice_id="Ryan"` |
+| 4 | `qwen_custom_voice`   | `0.6B`     | preset       | `preset_voice_id="Ryan"` |
+| 5 | `luxtts`              | —          | cloned       | English only |
+| 6 | `chatterbox`          | —          | cloned       | |
+| 7 | `chatterbox_turbo`    | —          | cloned       | English only |
+| 8 | `tada`                | `1B`       | cloned       | tada-1b, English only |
+| 9 | `tada`                | `3B`       | cloned       | tada-3b-ml, multilingual |
+| 10| `kokoro`              | —          | preset       | `preset_voice_id="af_heart"` |
+
+Cloned engines (1, 2, 5, 6, 7, 8, 9) share **one** profile created once with the reference WAV. Preset profiles are created separately, one for kokoro and one for qwen_custom_voice.
+
+Language for every run: `en` (covers every engine's supported set).
+
+## End-to-end flow
+
+```
+1. Resolve paths          → find binary, build if missing
+2. Launch binary          → spawn with --port --data-dir --parent-pid
+3. Wait for /health       → poll until status=="healthy" or 120s timeout
+4. Create profiles        → 1 cloned + 2 preset, via /profiles (+ /samples)
+5. For each (engine, model_size) in matrix:
+     a. Check cache       → GET /models/status → cached? short timeout : long
+     b. POST /generate    → get generation_id
+     c. Stream /status    → consume SSE until completed/failed/timeout
+     d. Record result     → {engine, model_size, status, duration, error, elapsed}
+6. Write results          → JSON + Markdown table to ./results/
+7. Shutdown binary        → SIGTERM, fall back to kill, verify port freed
+8. Exit code              → 0 if all passed, 1 otherwise
+```
+
+## Binary resolution
+
+Search order — **first hit wins**:
+
+| Platform | Path | Build type |
+|----------|------|------------|
+| macOS    | `backend/dist/voicebox-server-cuda/voicebox-server-cuda` | onedir (CUDA, rarely on Mac) |
+| macOS    | `backend/dist/voicebox-server`                          | onefile (CPU) |
+| Windows  | `backend\dist\voicebox-server-cuda\voicebox-server-cuda.exe` | onedir (CUDA) |
+| Windows  | `backend\dist\voicebox-server.exe`                      | onefile (CPU) |
+
+If none exist, run `python backend/build_binary.py` and wait for it to finish (can take 5-20 min). Fail with a clear error if the build itself fails. `--skip-build` flag forces "error out if no binary" instead of building.
+
+## Spawn command
+
+Mirrors Tauri's launch in `tauri/src-tauri/src/main.rs:369-388`:
+
+```
+<binary> --host 127.0.0.1 --port <free-port> --data-dir <tempdir> --parent-pid <test-pid>
+```
+
+- **Port**: bind to `0` first in Python to grab a free port, then pass that number.
+- **Data dir**: `tempfile.mkdtemp(prefix="voicebox-e2e-")`. Deleted after the run unless `--keep-data-dir`. Profiles and generated WAVs land here.
+- **Parent PID**: current Python PID — ensures the backend dies if the test crashes (watchdog in `server.py:102-224`).
+- **stdout/stderr**: tee to both a log file in `./results/server-<timestamp>.log` and a rolling in-memory buffer. On model failure, last 100 lines of the buffer are attached to that model's error record.
+
+## Profile setup
+
+One cloned profile shared across all cloning engines:
+
+```http
+POST /profiles
+{
+  "name": "e2e-cloned",
+  "voice_type": "cloned",
+  "language": "en"
+}
+```
+
+Then:
+
+```http
+POST /profiles/{id}/samples   (multipart)
+  file: <reference WAV>
+  reference_text: <exact transcription>
+```
+
+Two preset profiles:
+
+```http
+POST /profiles
+{ "name": "e2e-kokoro",  "voice_type": "preset", "language": "en",
+  "preset_engine": "kokoro",            "preset_voice_id": "af_heart" }
+
+POST /profiles
+{ "name": "e2e-qwen-cv", "voice_type": "preset", "language": "en",
+  "preset_engine": "qwen_custom_voice", "preset_voice_id": "Ryan" }
+```
+
+## Generation request (per matrix row)
+
+```http
+POST /generate
+{
+  "profile_id": "<appropriate profile>",
+  "text": "The quick brown fox jumps over the lazy dog.",
+  "language": "en",
+  "engine": "<engine>",
+  "model_size": "<size or omitted>",
+  "seed": 42,
+  "normalize": true
+}
+```
+
+Response `id` feeds into the SSE status loop (`GET /generate/{id}/status`, `routes/generations.py:190-227`). Loop reads lines until a payload with `status in ("completed", "failed")` arrives, then breaks.
+
+## Timeout strategy (split)
+
+Check `GET /models/status` for the target model **before** generation:
+
+| Cached? | Per-model timeout | Rationale |
+|---------|-------------------|-----------|
+| Yes     | **3 minutes**     | Inference only; generous for CPU builds |
+| No      | **20 minutes**    | First-run HF download up to 8 GB (tada-3b-ml) |
+
+On timeout: cancel the SSE stream, mark the row `timeout`, and continue to the next row. Don't abort the whole run on one timeout.
+
+## Result format
+
+`./results/e2e-<platform>-<arch>-<timestamp>.json`:
+
+```json
+{
+  "platform": "darwin-arm64",
+  "binary": "/abs/path/voicebox-server",
+  "binary_size_mb": 612,
+  "started_at": "2026-04-16T12:34:56Z",
+  "finished_at": "...",
+  "results": [
+    {
+      "engine": "qwen",
+      "model_size": "1.7B",
+      "status": "passed|failed|timeout",
+      "generation_id": "...",
+      "was_cached": true,
+      "elapsed_seconds": 12.4,
+      "audio_duration": 3.1,
+      "audio_path": "/tmp/.../gen.wav",
+      "error": null,
+      "server_log_tail": null
+    }
+  ]
+}
+```
+
+Companion `./results/e2e-<...>.md`:
+
+```
+# Voicebox E2E — darwin-arm64 — 2026-04-16 12:34
+
+| Engine              | Size | Status | Elapsed | Error |
+|---------------------|------|--------|---------|-------|
+| qwen                | 1.7B | PASS   |  12.4s  |       |
+| qwen                | 0.6B | FAIL   |   4.1s  | CUDA OOM: ... |
+...
+```
+
+## CLI flags
+
+```
+python -m backend.tests.test_all_models_e2e [flags]
+
+--binary PATH           Use this binary instead of auto-detecting
+--skip-build            Error if no binary found (no auto-build)
+--reference-wav PATH    Reference audio (default: backend/tests/fixtures/reference_voice.wav)
+--reference-text STR    Transcription (default: read from fixtures/reference_voice.txt)
+--only ENGINE[,...]     Run only these engines (e.g. kokoro,qwen)
+--skip ENGINE[,...]     Skip these engines
+--keep-data-dir         Don't delete tempdir after run
+--timeout-cached SEC    Override 180
+--timeout-download SEC  Override 1200
+--port N                Override auto-picked port
+--output-dir PATH       Default: backend/tests/results/
+```
+
+## File layout
+
+```
+backend/tests/
+├── E2E_MODEL_TEST_DESIGN.md       (this file)
+├── test_all_models_e2e.py         (main script, ~400-500 LoC)
+├── fixtures/
+│   ├── reference_voice.wav        (user-provided, ~5-15s clean speech)
+│   └── reference_voice.txt        (exact transcription)
+└── results/                       (gitignored)
+    ├── e2e-darwin-arm64-<ts>.json
+    ├── e2e-darwin-arm64-<ts>.md
+    └── server-<ts>.log
+```
+
+The script uses only stdlib + `httpx` (or `requests`) + `sseclient-py` — all already in `backend/requirements.txt`. No pytest to keep it invocable as a single command on fresh checkouts.
+
+## Safety & cleanup
+
+- Always kill the spawned binary in a `try/finally`. On Windows, `taskkill /F /T` the whole tree (Tauri does the same).
+- Verify the port is free on shutdown (Tauri port-reuse check in `main.rs:114-186` could otherwise pick up a ghost).
+- Don't touch the user's HF cache by default — let the server use `HF_HUB_CACHE` / `VOICEBOX_MODELS_DIR`. Passing `--isolated-cache` would point both env vars at the tempdir for a true cold-start run (opt-in only; would re-download every time).
+
+## Non-goals
+
+- Not validating audio quality (no WER, no waveform comparison). Pass = "endpoint returned `completed` and produced a non-empty WAV".
+- Not testing STT (Whisper), effects chains, channels, or streaming endpoints.
+- Not running on CI today — human-invoked on dev machines. CI integration is a follow-up once the script is stable.
+- No model unload between runs — models stay loaded; server manages its own eviction.
+- No version-drift check on the binary.
+- No `instruct` parameter exercised on qwen_custom_voice runs.

backend/tests/README.md

@@ -0,0 +1,58 @@
+# Backend Tests
+
+Manual test scripts for debugging and validating backend functionality.
+
+## Test Files
+
+### `test_generation_progress.py`
+Tests TTS generation with SSE progress monitoring to identify UX issues where users see download progress even when the model is already cached.
+
+**Usage:**
+```bash
+cd backend
+python tests/test_generation_progress.py
+```
+
+**Prerequisites:**
+- Server must be running (`python main.py`)
+- At least one voice profile must exist
+
+### `test_real_download.py`
+Tests real model download with SSE progress monitoring.
+
+**Usage:**
+```bash
+cd backend
+# Delete cache first to force fresh download
+rm -rf ~/.cache/huggingface/hub/models--openai--whisper-base
+python tests/test_real_download.py
+```
+
+**Prerequisites:**
+- Server must be running (`python main.py`)
+
+### `test_progress.py`
+Unit tests for ProgressManager and HFProgressTracker functionality.
+
+**Usage:**
+```bash
+cd backend
+python tests/test_progress.py
+```
+
+### `test_check_progress_state.py`
+Debugging script to inspect the internal state of ProgressManager and TaskManager.
+
+**Usage:**
+```bash
+cd backend
+python tests/test_check_progress_state.py
+```
+
+## Notes
+
+These are manual test scripts, not automated unit tests. They're designed for:
+- Debugging progress tracking issues
+- Validating SSE event streams
+- Monitoring real-time download behavior
+- Inspecting internal state during development

backend/tests/__init__.py

@@ -0,0 +1,6 @@
+"""
+Test suite for Voicebox backend.
+
+This directory contains manual test scripts for debugging and validating
+progress tracking, model downloads, and generation functionality.
+"""

backend/tests/fixtures/README.md

@@ -0,0 +1,16 @@
+# E2E Test Fixtures
+
+Place two files here before running `test_all_models_e2e.py`:
+
+- `reference_voice.wav` — a clean speech sample, mono, 16–24 kHz, ~5–15 seconds.
+- `reference_voice.txt` — the **exact** transcription of the WAV (single line, no trailing newline required).
+
+These are used to create a cloned voice profile for every cloning-capable engine (qwen, luxtts, chatterbox, chatterbox_turbo, tada). Keep them out of version control if they contain personal audio — this directory is not gitignored by default, so add them to `.gitignore` locally if needed.
+
+You can point the test at different files with:
+
+```
+python backend/tests/test_all_models_e2e.py \
+  --reference-wav /path/to/your.wav \
+  --reference-text "exact transcription here"
+```

backend/tests/test_all_models_e2e.py

@@ -0,0 +1,630 @@
+"""
+End-to-end model generation test.
+
+Exercises every TTS model against the frozen PyInstaller binary, captures
+per-model pass/fail, and writes a JSON + Markdown report.
+
+Usage:
+    python backend/tests/test_all_models_e2e.py [flags]
+
+See E2E_MODEL_TEST_DESIGN.md for the full design.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import platform
+import shutil
+import signal
+import socket
+import subprocess
+import sys
+import tempfile
+import threading
+import time
+from collections import deque
+from dataclasses import asdict, dataclass, field
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Optional
+
+import httpx
+
+
+REPO_ROOT = Path(__file__).resolve().parents[2]
+BACKEND_DIR = REPO_ROOT / "backend"
+DIST_DIR = BACKEND_DIR / "dist"
+FIXTURES_DIR = Path(__file__).resolve().parent / "fixtures"
+RESULTS_DIR = Path(__file__).resolve().parent / "results"
+
+
+# ── Test matrix ──────────────────────────────────────────────────────
+
+@dataclass(frozen=True)
+class MatrixRow:
+    label: str            # human-readable (appears in report)
+    engine: str           # /generate engine
+    model_size: Optional[str]  # /generate model_size (None = omit)
+    profile_kind: str     # "cloned" | "preset_kokoro" | "preset_qwen_cv"
+    model_name: str       # /models/status key for cache lookup
+
+
+MATRIX: list[MatrixRow] = [
+    MatrixRow("qwen 1.7B",              "qwen",              "1.7B", "cloned",          "qwen-tts-1.7B"),
+    MatrixRow("qwen 0.6B",              "qwen",              "0.6B", "cloned",          "qwen-tts-0.6B"),
+    MatrixRow("qwen_custom_voice 1.7B", "qwen_custom_voice", "1.7B", "preset_qwen_cv",  "qwen-custom-voice-1.7B"),
+    MatrixRow("qwen_custom_voice 0.6B", "qwen_custom_voice", "0.6B", "preset_qwen_cv",  "qwen-custom-voice-0.6B"),
+    MatrixRow("luxtts",                 "luxtts",            None,   "cloned",          "luxtts"),
+    MatrixRow("chatterbox",             "chatterbox",        None,   "cloned",          "chatterbox-tts"),
+    MatrixRow("chatterbox_turbo",       "chatterbox_turbo",  None,   "cloned",          "chatterbox-turbo"),
+    MatrixRow("tada 1B",                "tada",              "1B",   "cloned",          "tada-1b"),
+    MatrixRow("tada 3B",                "tada",              "3B",   "cloned",          "tada-3b-ml"),
+    MatrixRow("kokoro",                 "kokoro",            None,   "preset_kokoro",   "kokoro"),
+]
+
+TEXT = "The quick brown fox jumps over the lazy dog."
+DEFAULT_TIMEOUT_CACHED = 180
+DEFAULT_TIMEOUT_DOWNLOAD = 1200
+HEALTH_TIMEOUT = 120
+
+
+# ── Result record ────────────────────────────────────────────────────
+
+@dataclass
+class ModelResult:
+    label: str
+    engine: str
+    model_size: Optional[str]
+    status: str                      # "passed" | "failed" | "timeout"
+    was_cached: Optional[bool] = None
+    generation_id: Optional[str] = None
+    elapsed_seconds: float = 0.0
+    audio_duration: Optional[float] = None
+    audio_path: Optional[str] = None
+    audio_bytes: Optional[int] = None
+    error: Optional[str] = None
+    http_status: Optional[int] = None
+    server_log_tail: Optional[list[str]] = None
+
+
+# ── Binary resolution ────────────────────────────────────────────────
+
+def find_binary() -> Optional[Path]:
+    """Return the first existing binary in priority order, or None."""
+    is_win = platform.system() == "Windows"
+    exe = ".exe" if is_win else ""
+    candidates = [
+        DIST_DIR / "voicebox-server-cuda" / f"voicebox-server-cuda{exe}",
+        DIST_DIR / f"voicebox-server{exe}",
+    ]
+    for c in candidates:
+        if c.exists() and c.is_file():
+            return c
+    return None
+
+
+def build_binary() -> Path:
+    """Invoke build_binary.py and return the resulting binary path."""
+    print("[build] No frozen binary found — invoking build_binary.py (this may take 5-20 minutes)...", flush=True)
+    script = BACKEND_DIR / "build_binary.py"
+    result = subprocess.run(
+        [sys.executable, str(script)],
+        cwd=str(BACKEND_DIR),
+    )
+    if result.returncode != 0:
+        raise RuntimeError(f"build_binary.py exited with code {result.returncode}")
+    found = find_binary()
+    if found is None:
+        raise RuntimeError("build_binary.py finished but no binary was found in backend/dist/")
+    return found
+
+
+# ── Server spawn + log capture ───────────────────────────────────────
+
+class ServerProcess:
+    def __init__(self, binary: Path, port: int, data_dir: Path, log_path: Path):
+        self.binary = binary
+        self.port = port
+        self.data_dir = data_dir
+        self.log_path = log_path
+        self.proc: Optional[subprocess.Popen] = None
+        self._log_buffer: deque[str] = deque(maxlen=500)
+        self._reader_thread: Optional[threading.Thread] = None
+
+    def start(self) -> None:
+        args = [
+            str(self.binary),
+            "--host", "127.0.0.1",
+            "--port", str(self.port),
+            "--data-dir", str(self.data_dir),
+            "--parent-pid", str(os.getpid()),
+        ]
+        print(f"[spawn] {' '.join(args)}", flush=True)
+        self._log_fh = open(self.log_path, "w", encoding="utf-8", errors="replace")
+        # Combine stderr into stdout so we get a single ordered stream.
+        self.proc = subprocess.Popen(
+            args,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            bufsize=1,
+            text=True,
+            errors="replace",
+        )
+        self._reader_thread = threading.Thread(target=self._pump_logs, daemon=True)
+        self._reader_thread.start()
+
+    def _pump_logs(self) -> None:
+        assert self.proc is not None and self.proc.stdout is not None
+        for line in self.proc.stdout:
+            self._log_buffer.append(line.rstrip("\n"))
+            self._log_fh.write(line)
+            self._log_fh.flush()
+
+    def log_tail(self, n: int = 100) -> list[str]:
+        tail = list(self._log_buffer)[-n:]
+        return tail
+
+    def is_alive(self) -> bool:
+        return self.proc is not None and self.proc.poll() is None
+
+    def stop(self) -> None:
+        if self.proc is None:
+            return
+        if self.proc.poll() is not None:
+            return
+        try:
+            if platform.system() == "Windows":
+                subprocess.run(
+                    ["taskkill", "/F", "/T", "/PID", str(self.proc.pid)],
+                    capture_output=True,
+                )
+            else:
+                self.proc.send_signal(signal.SIGTERM)
+        except Exception as e:
+            print(f"[shutdown] signal failed: {e}", flush=True)
+        try:
+            self.proc.wait(timeout=10)
+        except subprocess.TimeoutExpired:
+            print("[shutdown] server didn't exit cleanly, killing", flush=True)
+            self.proc.kill()
+            try:
+                self.proc.wait(timeout=5)
+            except subprocess.TimeoutExpired:
+                pass
+        if self._reader_thread is not None:
+            self._reader_thread.join(timeout=2)
+        try:
+            self._log_fh.close()
+        except Exception:
+            pass
+
+
+def pick_free_port() -> int:
+    s = socket.socket()
+    s.bind(("127.0.0.1", 0))
+    port = s.getsockname()[1]
+    s.close()
+    return port
+
+
+# ── HTTP helpers ─────────────────────────────────────────────────────
+
+def wait_for_health(base_url: str, server: ServerProcess, timeout: int) -> None:
+    deadline = time.time() + timeout
+    with httpx.Client(timeout=5.0) as client:
+        while time.time() < deadline:
+            if not server.is_alive():
+                raise RuntimeError("Server process exited before becoming healthy")
+            try:
+                r = client.get(f"{base_url}/health")
+                if r.status_code == 200 and r.json().get("status") == "healthy":
+                    return
+            except httpx.HTTPError:
+                pass
+            time.sleep(1.0)
+    raise TimeoutError(f"Server did not become healthy within {timeout}s")
+
+
+def get_model_cached(client: httpx.Client, base_url: str, model_name: str) -> Optional[bool]:
+    try:
+        r = client.get(f"{base_url}/models/status", timeout=30.0)
+        r.raise_for_status()
+        for m in r.json().get("models", []):
+            if m.get("model_name") == model_name:
+                return bool(m.get("downloaded"))
+    except httpx.HTTPError:
+        return None
+    return None
+
+
+def create_cloned_profile(client: httpx.Client, base_url: str, wav_path: Path, reference_text: str) -> str:
+    r = client.post(f"{base_url}/profiles", json={
+        "name": "e2e-cloned",
+        "voice_type": "cloned",
+        "language": "en",
+    })
+    r.raise_for_status()
+    profile_id = r.json()["id"]
+
+    with open(wav_path, "rb") as f:
+        r = client.post(
+            f"{base_url}/profiles/{profile_id}/samples",
+            files={"file": (wav_path.name, f, "audio/wav")},
+            data={"reference_text": reference_text},
+            timeout=120.0,
+        )
+    r.raise_for_status()
+    return profile_id
+
+
+def create_preset_profile(client: httpx.Client, base_url: str, name: str, engine: str, voice_id: str) -> str:
+    r = client.post(f"{base_url}/profiles", json={
+        "name": name,
+        "voice_type": "preset",
+        "language": "en",
+        "preset_engine": engine,
+        "preset_voice_id": voice_id,
+    })
+    r.raise_for_status()
+    return r.json()["id"]
+
+
+def run_one_generation(
+    client: httpx.Client,
+    base_url: str,
+    row: MatrixRow,
+    profile_id: str,
+    timeout_s: int,
+) -> tuple[str, dict]:
+    """Start a generation and stream its status until done/failed/timeout.
+
+    Returns (status, payload) where status is "completed" | "failed" | "timeout".
+    """
+    body = {
+        "profile_id": profile_id,
+        "text": TEXT,
+        "language": "en",
+        "engine": row.engine,
+        "seed": 42,
+        "normalize": True,
+    }
+    if row.model_size is not None:
+        body["model_size"] = row.model_size
+
+    r = client.post(f"{base_url}/generate", json=body, timeout=30.0)
+    r.raise_for_status()
+    gen = r.json()
+    gen_id = gen["id"]
+
+    deadline = time.time() + timeout_s
+    last_payload: dict = gen
+    status_url = f"{base_url}/generate/{gen_id}/status"
+
+    while time.time() < deadline:
+        remaining = max(1.0, deadline - time.time())
+        try:
+            with client.stream("GET", status_url, timeout=httpx.Timeout(remaining + 5, read=remaining + 5)) as resp:
+                resp.raise_for_status()
+                for line in resp.iter_lines():
+                    if not line or not line.startswith("data: "):
+                        continue
+                    try:
+                        payload = json.loads(line[6:])
+                    except json.JSONDecodeError:
+                        continue
+                    last_payload = payload
+                    status = payload.get("status")
+                    if status == "not_found":
+                        return "failed", {"error": "generation not found", **payload}
+                    if status in ("completed", "failed"):
+                        return status, payload
+                    if time.time() >= deadline:
+                        break
+        except httpx.HTTPError:
+            time.sleep(1.0)
+            continue
+
+    return "timeout", last_payload
+
+
+def fetch_audio_info(
+    client: httpx.Client, base_url: str, generation_id: str, data_dir: Path
+) -> tuple[Optional[str], Optional[int]]:
+    """Return (audio_path, audio_bytes) for a completed generation.
+
+    Server stores audio_path relative to data_dir; resolve it to get a size.
+    """
+    try:
+        r = client.get(f"{base_url}/history/{generation_id}", timeout=10.0)
+        if r.status_code != 200:
+            return None, None
+        data = r.json()
+        audio_path = data.get("audio_path")
+        if not audio_path:
+            return None, None
+        p = Path(audio_path)
+        if not p.is_absolute():
+            p = data_dir / p
+        if p.exists():
+            return str(p), p.stat().st_size
+        return audio_path, None
+    except httpx.HTTPError:
+        return None, None
+
+
+# ── Report writers ───────────────────────────────────────────────────
+
+def write_reports(
+    output_dir: Path,
+    binary: Path,
+    started_at: datetime,
+    finished_at: datetime,
+    results: list[ModelResult],
+) -> tuple[Path, Path]:
+    output_dir.mkdir(parents=True, exist_ok=True)
+    plat = f"{platform.system().lower()}-{platform.machine().lower()}"
+    ts = started_at.strftime("%Y%m%d-%H%M%S")
+    json_path = output_dir / f"e2e-{plat}-{ts}.json"
+    md_path = output_dir / f"e2e-{plat}-{ts}.md"
+
+    doc = {
+        "platform": plat,
+        "binary": str(binary),
+        "binary_size_mb": round(binary.stat().st_size / (1024 * 1024), 1) if binary.exists() else None,
+        "started_at": started_at.isoformat(),
+        "finished_at": finished_at.isoformat(),
+        "elapsed_seconds": (finished_at - started_at).total_seconds(),
+        "results": [asdict(r) for r in results],
+    }
+    json_path.write_text(json.dumps(doc, indent=2))
+
+    lines = [
+        f"# Voicebox E2E — {plat} — {started_at.strftime('%Y-%m-%d %H:%M UTC')}",
+        "",
+        f"Binary: `{binary}`  ",
+        f"Elapsed: {doc['elapsed_seconds']:.1f}s",
+        "",
+        "| Model | Status | Cached | Elapsed | Audio | Error |",
+        "|-------|--------|--------|---------|-------|-------|",
+    ]
+    for r in results:
+        status_icon = {"passed": "PASS", "failed": "FAIL", "timeout": "TIMEOUT"}.get(r.status, r.status.upper())
+        cached = "yes" if r.was_cached else ("no" if r.was_cached is False else "?")
+        audio_col = f"{r.audio_duration:.2f}s" if r.audio_duration else ("—" if r.status != "passed" else "?")
+        error_col = (r.error or "").replace("\n", " ")[:120]
+        lines.append(f"| {r.label} | {status_icon} | {cached} | {r.elapsed_seconds:.1f}s | {audio_col} | {error_col} |")
+
+    failed_rows = [r for r in results if r.status != "passed"]
+    if failed_rows:
+        lines.append("")
+        lines.append("## Failures")
+        for r in failed_rows:
+            lines.append("")
+            lines.append(f"### {r.label} — {r.status}")
+            if r.error:
+                lines.append("")
+                lines.append("```")
+                lines.append(r.error)
+                lines.append("```")
+            if r.server_log_tail:
+                lines.append("")
+                lines.append("<details><summary>server log (last lines)</summary>")
+                lines.append("")
+                lines.append("```")
+                lines.extend(r.server_log_tail)
+                lines.append("```")
+                lines.append("</details>")
+
+    md_path.write_text("\n".join(lines) + "\n")
+    return json_path, md_path
+
+
+# ── Main ─────────────────────────────────────────────────────────────
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description="Voicebox E2E model generation test")
+    p.add_argument("--binary", type=Path, help="Path to voicebox-server binary (overrides auto-detect)")
+    p.add_argument("--skip-build", action="store_true", help="Error if binary missing instead of building")
+    p.add_argument(
+        "--reference-wav",
+        type=Path,
+        default=FIXTURES_DIR / "reference_voice.wav",
+        help="Reference audio for cloning engines",
+    )
+    p.add_argument(
+        "--reference-text",
+        help="Transcription of reference-wav (default: read from fixtures/reference_voice.txt)",
+    )
+    p.add_argument("--only", help="Comma-separated engines to run (e.g. kokoro,qwen)")
+    p.add_argument("--skip", help="Comma-separated engines to skip")
+    p.add_argument("--keep-data-dir", action="store_true", help="Don't delete tempdir after run")
+    p.add_argument("--timeout-cached", type=int, default=DEFAULT_TIMEOUT_CACHED)
+    p.add_argument("--timeout-download", type=int, default=DEFAULT_TIMEOUT_DOWNLOAD)
+    p.add_argument("--port", type=int, help="Override auto-picked port")
+    p.add_argument("--output-dir", type=Path, default=RESULTS_DIR)
+    return p.parse_args()
+
+
+def filter_matrix(args: argparse.Namespace) -> list[MatrixRow]:
+    only = set(x.strip() for x in args.only.split(",")) if args.only else None
+    skip = set(x.strip() for x in args.skip.split(",")) if args.skip else set()
+    rows = []
+    for r in MATRIX:
+        if only is not None and r.engine not in only:
+            continue
+        if r.engine in skip:
+            continue
+        rows.append(r)
+    return rows
+
+
+def resolve_reference(args: argparse.Namespace) -> tuple[Path, str]:
+    wav = args.reference_wav
+    if not wav.exists():
+        raise FileNotFoundError(
+            f"Reference WAV not found: {wav}\n"
+            f"Place a sample at {FIXTURES_DIR / 'reference_voice.wav'} or pass --reference-wav.\n"
+            f"See backend/tests/fixtures/README.md."
+        )
+    if args.reference_text:
+        text = args.reference_text
+    else:
+        txt_path = wav.with_suffix(".txt")
+        if not txt_path.exists():
+            raise FileNotFoundError(
+                f"Reference transcription not found: {txt_path}\n"
+                f"Create it next to the WAV, or pass --reference-text."
+            )
+        text = txt_path.read_text().strip()
+    if not text:
+        raise ValueError("Reference transcription is empty")
+    return wav, text
+
+
+def main() -> int:
+    args = parse_args()
+    rows = filter_matrix(args)
+    if not rows:
+        print("No rows selected after --only/--skip filtering", file=sys.stderr)
+        return 2
+
+    # Binary
+    binary = args.binary or find_binary()
+    if binary is None:
+        if args.skip_build:
+            print("No frozen binary found and --skip-build set. Run: python backend/build_binary.py", file=sys.stderr)
+            return 2
+        binary = build_binary()
+    if not binary.exists():
+        print(f"Binary path does not exist: {binary}", file=sys.stderr)
+        return 2
+    print(f"[binary] {binary}", flush=True)
+
+    # Reference audio (only required if any cloning row is in the matrix)
+    needs_reference = any(r.profile_kind == "cloned" for r in rows)
+    ref_wav: Optional[Path] = None
+    ref_text: Optional[str] = None
+    if needs_reference:
+        try:
+            ref_wav, ref_text = resolve_reference(args)
+        except (FileNotFoundError, ValueError) as e:
+            print(f"[fixture] {e}", file=sys.stderr)
+            return 2
+        print(f"[fixture] reference WAV:  {ref_wav}", flush=True)
+        print(f"[fixture] reference text: {ref_text!r}", flush=True)
+
+    # Tempdir + log path
+    data_dir = Path(tempfile.mkdtemp(prefix="voicebox-e2e-"))
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+    ts = datetime.now(timezone.utc).strftime("%Y%m%d-%H%M%S")
+    log_path = args.output_dir / f"server-{ts}.log"
+
+    port = args.port or pick_free_port()
+    base_url = f"http://127.0.0.1:{port}"
+
+    server = ServerProcess(binary=binary, port=port, data_dir=data_dir, log_path=log_path)
+    started_at = datetime.now(timezone.utc)
+    results: list[ModelResult] = []
+
+    try:
+        server.start()
+        print(f"[health] waiting for {base_url}/health ...", flush=True)
+        wait_for_health(base_url, server, HEALTH_TIMEOUT)
+        print("[health] ready", flush=True)
+
+        with httpx.Client(timeout=30.0) as client:
+            # Profile setup (only create what's needed)
+            cloned_profile_id: Optional[str] = None
+            kokoro_profile_id: Optional[str] = None
+            qwen_cv_profile_id: Optional[str] = None
+            needed_kinds = {r.profile_kind for r in rows}
+            if "cloned" in needed_kinds:
+                assert ref_wav is not None and ref_text is not None
+                print("[profile] creating cloned profile...", flush=True)
+                cloned_profile_id = create_cloned_profile(client, base_url, ref_wav, ref_text)
+            if "preset_kokoro" in needed_kinds:
+                print("[profile] creating kokoro preset...", flush=True)
+                kokoro_profile_id = create_preset_profile(client, base_url, "e2e-kokoro", "kokoro", "af_heart")
+            if "preset_qwen_cv" in needed_kinds:
+                print("[profile] creating qwen_custom_voice preset...", flush=True)
+                qwen_cv_profile_id = create_preset_profile(client, base_url, "e2e-qwen-cv", "qwen_custom_voice", "Ryan")
+
+            profile_lookup = {
+                "cloned": cloned_profile_id,
+                "preset_kokoro": kokoro_profile_id,
+                "preset_qwen_cv": qwen_cv_profile_id,
+            }
+
+            # Matrix loop
+            for row in rows:
+                print(f"\n[run] {row.label} (engine={row.engine}, size={row.model_size})", flush=True)
+                profile_id = profile_lookup[row.profile_kind]
+                assert profile_id is not None
+                was_cached = get_model_cached(client, base_url, row.model_name)
+                timeout_s = args.timeout_cached if was_cached else args.timeout_download
+                print(f"[run] cached={was_cached} timeout={timeout_s}s", flush=True)
+
+                t0 = time.time()
+                result = ModelResult(
+                    label=row.label,
+                    engine=row.engine,
+                    model_size=row.model_size,
+                    status="failed",
+                    was_cached=was_cached,
+                )
+                try:
+                    status, payload = run_one_generation(client, base_url, row, profile_id, timeout_s)
+                    result.status = "passed" if status == "completed" else status
+                    result.generation_id = payload.get("id")
+                    result.audio_duration = payload.get("duration")
+                    result.error = payload.get("error")
+                    if status == "completed" and result.generation_id:
+                        audio_path, audio_bytes = fetch_audio_info(
+                            client, base_url, result.generation_id, data_dir
+                        )
+                        result.audio_path = audio_path
+                        result.audio_bytes = audio_bytes
+                        if audio_bytes is not None and audio_bytes == 0:
+                            result.status = "failed"
+                            result.error = (result.error or "") + " (audio file is empty)"
+                except httpx.HTTPStatusError as e:
+                    result.status = "failed"
+                    result.http_status = e.response.status_code
+                    try:
+                        detail = e.response.json().get("detail")
+                    except Exception:
+                        detail = e.response.text
+                    result.error = f"HTTP {e.response.status_code}: {detail}"
+                except Exception as e:
+                    result.status = "failed"
+                    result.error = f"{type(e).__name__}: {e}"
+
+                result.elapsed_seconds = round(time.time() - t0, 2)
+                if result.status != "passed":
+                    result.server_log_tail = server.log_tail(100)
+                print(f"[run] {row.label} → {result.status} in {result.elapsed_seconds}s"
+                      + (f" ({result.error})" if result.error else ""), flush=True)
+                results.append(result)
+    finally:
+        finished_at = datetime.now(timezone.utc)
+        server.stop()
+        if not args.keep_data_dir:
+            shutil.rmtree(data_dir, ignore_errors=True)
+        else:
+            print(f"[cleanup] keeping data dir: {data_dir}", flush=True)
+
+    json_path, md_path = write_reports(args.output_dir, binary, started_at, finished_at, results)
+    print(f"\n[report] {json_path}")
+    print(f"[report] {md_path}")
+    print(f"[report] server log: {log_path}")
+
+    passed = sum(1 for r in results if r.status == "passed")
+    failed = len(results) - passed
+    print(f"\n== {passed} passed, {failed} failed ==")
+    return 0 if failed == 0 else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())

backend/tests/test_audio_preprocess.py

@@ -0,0 +1,112 @@
+"""
+Unit tests for reference-audio preprocessing.
+
+Covers :func:`backend.utils.audio.preprocess_reference_audio` and
+:func:`backend.utils.audio.validate_and_load_reference_audio`.
+"""
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pytest
+import soundfile as sf
+
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from utils.audio import (  # noqa: E402
+    preprocess_reference_audio,
+    validate_and_load_reference_audio,
+)
+
+
+SR = 24000
+
+
+def _tone(duration_s: float, amp: float = 0.3, freq: float = 220.0) -> np.ndarray:
+    n = int(duration_s * SR)
+    t = np.arange(n, dtype=np.float32) / SR
+    return (amp * np.sin(2 * np.pi * freq * t)).astype(np.float32)
+
+
+def test_peak_cap_scales_hot_input():
+    audio = _tone(3.0, amp=0.99)
+    out = preprocess_reference_audio(audio, SR)
+    assert np.abs(out).max() <= 0.951
+
+
+def test_peak_cap_leaves_moderate_input_untouched():
+    audio = _tone(3.0, amp=0.5)
+    out = preprocess_reference_audio(audio, SR)
+    assert np.isclose(np.abs(out).max(), 0.5, atol=1e-3)
+
+
+def test_dc_offset_removed():
+    audio = _tone(3.0, amp=0.3) + 0.1
+    out = preprocess_reference_audio(audio, SR)
+    assert abs(float(np.mean(out))) < 1e-3
+
+
+def test_silence_is_trimmed_with_padding_kept():
+    silence = np.zeros(int(SR * 1.0), dtype=np.float32)
+    speech = _tone(3.0, amp=0.3)
+    audio = np.concatenate([silence, speech, silence])
+    out = preprocess_reference_audio(audio, SR)
+    # Most of the 2s of leading/trailing silence should be gone, but the
+    # 3s of speech plus ~200ms of padding should remain.
+    assert len(audio) - len(out) >= SR, "expected >=1s of silence trimmed"
+    assert len(out) >= int(3.0 * SR), "speech body should be preserved"
+
+
+def test_clean_audio_is_not_padded_past_original_length():
+    # Well-recorded audio with no edge silence shouldn't get longer after
+    # preprocessing — otherwise a 29.9 s upload could be pushed past the
+    # 30 s max_duration ceiling downstream.
+    audio = _tone(3.0, amp=0.3)
+    out = preprocess_reference_audio(audio, SR)
+    assert len(out) <= len(audio)
+
+
+def test_empty_input_returns_empty():
+    out = preprocess_reference_audio(np.zeros(0, dtype=np.float32), SR)
+    assert out.size == 0
+
+
+def test_validate_accepts_previously_rejected_hot_file(tmp_path):
+    audio = _tone(3.0, amp=0.995)
+    path = tmp_path / "hot.wav"
+    sf.write(str(path), audio, SR)
+
+    ok, err, out_audio, out_sr = validate_and_load_reference_audio(str(path))
+
+    assert ok, f"expected pass, got error: {err}"
+    assert out_audio is not None
+    assert out_sr == SR
+    assert np.abs(out_audio).max() <= 0.951
+
+
+def test_validate_still_rejects_silent_input(tmp_path):
+    audio = np.zeros(int(SR * 3.0), dtype=np.float32)
+    path = tmp_path / "silent.wav"
+    sf.write(str(path), audio, SR)
+
+    ok, err, _, _ = validate_and_load_reference_audio(str(path))
+
+    assert not ok
+    assert err is not None
+    assert "too short" in err.lower() or "quiet" in err.lower()
+
+
+def test_validate_rejects_too_short(tmp_path):
+    audio = _tone(0.5, amp=0.3)
+    path = tmp_path / "short.wav"
+    sf.write(str(path), audio, SR)
+
+    ok, err, _, _ = validate_and_load_reference_audio(str(path))
+
+    assert not ok
+    assert "too short" in (err or "").lower()
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])

backend/tests/test_cors.py

@@ -0,0 +1,162 @@
+"""
+Tests for CORS origin restrictions.
+
+Validates that the CORS middleware only allows known local origins
+and respects the VOICEBOX_CORS_ORIGINS environment variable.
+
+Uses a minimal FastAPI app that mirrors the exact CORS configuration
+from backend/main.py, so tests run without heavy ML dependencies.
+
+Usage:
+    pip install httpx pytest fastapi starlette
+    python -m pytest backend/tests/test_cors.py -v
+"""
+
+import os
+import pytest
+from unittest.mock import patch
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from starlette.testclient import TestClient
+
+
+def _build_app(env_origins: str = "") -> FastAPI:
+    """
+    Build a minimal FastAPI app with the same CORS logic as backend/main.py.
+
+    This mirrors the exact code in main.py so the test validates the real
+    configuration without needing torch/numpy/transformers installed.
+    """
+    app = FastAPI()
+
+    _default_origins = [
+        "http://localhost:5173",
+        "http://127.0.0.1:5173",
+        "http://localhost:17493",
+        "http://127.0.0.1:17493",
+        "tauri://localhost",
+        "https://tauri.localhost",
+    ]
+    _cors_origins = _default_origins + [o.strip() for o in env_origins.split(",") if o.strip()]
+
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=_cors_origins,
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+
+    @app.get("/health")
+    async def health():
+        return {"status": "ok"}
+
+    return app
+
+
+@pytest.fixture()
+def client():
+    return TestClient(_build_app())
+
+
+@pytest.fixture()
+def client_with_custom_origins():
+    return TestClient(_build_app("https://custom.example.com,https://other.example.com"))
+
+
+def _get_with_origin(client: TestClient, origin: str) -> dict:
+    """Send a GET with Origin header, return response headers."""
+    response = client.get("/health", headers={"Origin": origin})
+    return dict(response.headers)
+
+
+def _preflight(client: TestClient, origin: str) -> dict:
+    """Send CORS preflight OPTIONS request, return response headers."""
+    response = client.options(
+        "/health",
+        headers={
+            "Origin": origin,
+            "Access-Control-Request-Method": "GET",
+        },
+    )
+    return dict(response.headers)
+
+
+class TestCORSDefaultOrigins:
+    """CORS should allow known local origins and block everything else."""
+
+    @pytest.mark.parametrize("origin", [
+        "http://localhost:5173",
+        "http://127.0.0.1:5173",
+        "http://localhost:17493",
+        "http://127.0.0.1:17493",
+        "tauri://localhost",
+        "https://tauri.localhost",
+    ])
+    def test_allowed_origins(self, client, origin):
+        headers = _get_with_origin(client, origin)
+        assert headers.get("access-control-allow-origin") == origin
+
+    @pytest.mark.parametrize("origin", [
+        "http://evil.com",
+        "http://localhost:9999",
+        "https://attacker.example.com",
+        "null",
+    ])
+    def test_blocked_origins(self, client, origin):
+        headers = _get_with_origin(client, origin)
+        assert "access-control-allow-origin" not in headers
+
+    def test_preflight_allowed(self, client):
+        headers = _preflight(client, "http://localhost:5173")
+        assert headers.get("access-control-allow-origin") == "http://localhost:5173"
+
+    def test_preflight_blocked(self, client):
+        headers = _preflight(client, "http://evil.com")
+        assert "access-control-allow-origin" not in headers
+
+    def test_credentials_header_present(self, client):
+        headers = _get_with_origin(client, "http://localhost:5173")
+        assert headers.get("access-control-allow-credentials") == "true"
+
+
+class TestCORSCustomOrigins:
+    """VOICEBOX_CORS_ORIGINS env var should extend the allowlist."""
+
+    def test_custom_origin_allowed(self, client_with_custom_origins):
+        headers = _get_with_origin(client_with_custom_origins, "https://custom.example.com")
+        assert headers.get("access-control-allow-origin") == "https://custom.example.com"
+
+    def test_other_custom_origin_allowed(self, client_with_custom_origins):
+        headers = _get_with_origin(client_with_custom_origins, "https://other.example.com")
+        assert headers.get("access-control-allow-origin") == "https://other.example.com"
+
+    def test_default_origins_still_work(self, client_with_custom_origins):
+        headers = _get_with_origin(client_with_custom_origins, "http://localhost:5173")
+        assert headers.get("access-control-allow-origin") == "http://localhost:5173"
+
+    def test_unlisted_origin_still_blocked(self, client_with_custom_origins):
+        headers = _get_with_origin(client_with_custom_origins, "http://evil.com")
+        assert "access-control-allow-origin" not in headers
+
+
+class TestCORSEnvVarParsing:
+    """Edge cases for VOICEBOX_CORS_ORIGINS parsing."""
+
+    def test_empty_env_var(self):
+        app = _build_app("")
+        client = TestClient(app)
+        headers = _get_with_origin(client, "http://evil.com")
+        assert "access-control-allow-origin" not in headers
+
+    def test_whitespace_trimmed(self):
+        app = _build_app("  https://spaced.example.com  ")
+        client = TestClient(app)
+        headers = _get_with_origin(client, "https://spaced.example.com")
+        assert headers.get("access-control-allow-origin") == "https://spaced.example.com"
+
+    def test_trailing_comma_ignored(self):
+        app = _build_app("https://one.example.com,")
+        client = TestClient(app)
+        headers = _get_with_origin(client, "https://one.example.com")
+        assert headers.get("access-control-allow-origin") == "https://one.example.com"

backend/tests/test_generation_download.py

@@ -0,0 +1,305 @@
+"""
+Test TTS generation with SSE progress monitoring.
+This test captures the exact SSE events triggered during generation
+to identify UX issues where users see download progress even when
+the model is already cached.
+"""
+
+import asyncio
+import json
+import httpx
+from typing import List, Dict, Optional
+from datetime import datetime
+
+
+async def monitor_sse_stream(model_name: str, timeout: int = 120):
+    """Monitor SSE stream for a model during generation."""
+    events: List[Dict] = []
+    url = f"http://localhost:8000/models/progress/{model_name}"
+
+    print(f"[{_timestamp()}] Connecting to SSE endpoint: {url}")
+
+    try:
+        async with httpx.AsyncClient(timeout=timeout) as client:
+            async with client.stream("GET", url) as response:
+                print(f"[{_timestamp()}] SSE connected, status: {response.status_code}")
+
+                if response.status_code != 200:
+                    print(f"[{_timestamp()}] Error: SSE endpoint returned {response.status_code}")
+                    return events
+
+                async for line in response.aiter_lines():
+                    if not line:
+                        continue
+
+                    timestamp = _timestamp()
+
+                    if line.startswith("data: "):
+                        try:
+                            data = json.loads(line[6:])
+                            print(
+                                f"[{timestamp}] → SSE Event: {data['status']:12} {data.get('progress', 0):6.1f}% {data.get('filename', '')}"
+                            )
+                            events.append({**data, "_timestamp": timestamp})
+
+                            # Stop if complete or error
+                            if data.get("status") in ("complete", "error"):
+                                print(f"[{timestamp}] → Model {data['status']}!")
+                                break
+
+                        except json.JSONDecodeError as e:
+                            print(f"[{timestamp}] Error parsing JSON: {e}")
+                            print(f"  Line was: {line}")
+
+                    elif line.startswith(": heartbeat"):
+                        print(f"[{timestamp}] ♥ heartbeat")
+
+    except asyncio.TimeoutError:
+        print(f"[{_timestamp()}] SSE monitoring timed out")
+    except Exception as e:
+        print(f"[{_timestamp()}] SSE error: {e}")
+
+    return events
+
+
+async def trigger_generation(profile_id: str, text: str, model_size: str = "1.7B"):
+    """Trigger TTS generation via the API."""
+    url = "http://localhost:8000/generate"
+
+    print(f"\n[{_timestamp()}] Triggering generation...")
+    print(f"  Profile: {profile_id}")
+    print(f"  Text: {text[:50]}...")
+    print(f"  Model: {model_size}")
+
+    try:
+        async with httpx.AsyncClient(timeout=120) as client:
+            response = await client.post(
+                url,
+                json={
+                    "profile_id": profile_id,
+                    "text": text,
+                    "language": "en",
+                    "model_size": model_size,
+                },
+            )
+
+            print(f"[{_timestamp()}] Response: {response.status_code}")
+
+            if response.status_code == 200:
+                result = response.json()
+                print(f"[{_timestamp()}] ✓ Generation successful!")
+                print(f"  Generation ID: {result.get('id')}")
+                print(f"  Duration: {result.get('duration', 0):.2f}s")
+                return True, result
+            elif response.status_code == 202:
+                # Model is being downloaded
+                result = response.json()
+                print(f"[{_timestamp()}] → Model download in progress")
+                print(f"  Detail: {result}")
+                return False, result
+            else:
+                print(f"[{_timestamp()}] ✗ Error: {response.text}")
+                return False, None
+
+    except Exception as e:
+        print(f"[{_timestamp()}] ✗ Exception: {e}")
+        return False, None
+
+
+async def get_first_profile():
+    """Get the first available voice profile."""
+    url = "http://localhost:8000/profiles"
+
+    try:
+        async with httpx.AsyncClient(timeout=10) as client:
+            response = await client.get(url)
+            if response.status_code == 200:
+                profiles = response.json()
+                if profiles:
+                    return profiles[0]["id"]
+    except Exception as e:
+        print(f"Error getting profiles: {e}")
+
+    return None
+
+
+async def check_server():
+    """Check if the server is running."""
+    try:
+        async with httpx.AsyncClient(timeout=5) as client:
+            response = await client.get("http://localhost:8000/health")
+            return response.status_code == 200
+    except Exception as e:
+        print(f"Server not running: {e}")
+        return False
+
+
+def _timestamp():
+    """Get current timestamp for logging."""
+    return datetime.now().strftime("%H:%M:%S.%f")[:-3]
+
+
+async def test_generation_with_cached_model():
+    """
+    Test Case 1: Generation when model is already cached.
+
+    This should NOT show any download progress events.
+    If it does, that's the UX bug we're trying to fix.
+    """
+    print("\n" + "=" * 80)
+    print("TEST CASE 1: Generation with Cached Model")
+    print("=" * 80)
+    print("Expected: No download progress events (or minimal/instant completion)")
+    print("Actual UX Issue: Users see 'started' and 'finished' events even for cached models")
+    print("=" * 80)
+
+    model_size = "1.7B"
+    model_name = f"qwen-tts-{model_size}"
+
+    # Get a profile
+    profile_id = await get_first_profile()
+    if not profile_id:
+        print("✗ No voice profiles found. Please create a profile first.")
+        return False
+
+    print(f"\nUsing profile: {profile_id}")
+
+    # Start SSE monitor BEFORE triggering generation
+    monitor_task = asyncio.create_task(monitor_sse_stream(model_name, timeout=30))
+
+    # Wait for SSE to connect
+    await asyncio.sleep(1)
+
+    # Trigger generation
+    test_text = "Hello, this is a test of the voice generation system."
+    success, result = await trigger_generation(profile_id, test_text, model_size)
+
+    if not success and result and result.get("downloading"):
+        print("\n⚠ Model is being downloaded. Waiting for download to complete...")
+        # Wait for SSE monitor to capture download events
+        events = await monitor_task
+        return events
+
+    # Wait a bit more to catch any progress events
+    await asyncio.sleep(3)
+
+    # Cancel SSE monitor
+    monitor_task.cancel()
+    try:
+        events = await monitor_task
+    except asyncio.CancelledError:
+        events = []
+
+    return events
+
+
+async def test_generation_with_fresh_download():
+    """
+    Test Case 2: Generation when model needs to be downloaded.
+
+    This SHOULD show download progress events.
+    """
+    print("\n" + "=" * 80)
+    print("TEST CASE 2: Generation with Model Download")
+    print("=" * 80)
+    print("Expected: Download progress events from 0% to 100%")
+    print("=" * 80)
+
+    # Use a different model size to force download
+    model_size = "0.6B"  # Smaller model for faster testing
+    model_name = f"qwen-tts-{model_size}"
+
+    # Get a profile
+    profile_id = await get_first_profile()
+    if not profile_id:
+        print("✗ No voice profiles found. Please create a profile first.")
+        return False
+
+    print(f"\nUsing profile: {profile_id}")
+    print("Note: This will download the model if not cached")
+
+    # Start SSE monitor BEFORE triggering generation
+    monitor_task = asyncio.create_task(monitor_sse_stream(model_name, timeout=300))
+
+    # Wait for SSE to connect
+    await asyncio.sleep(1)
+
+    # Trigger generation
+    test_text = "This should trigger a model download if the model is not cached."
+    success, result = await trigger_generation(profile_id, test_text, model_size)
+
+    if not success and result and result.get("downloading"):
+        print("\n→ Model download initiated. Monitoring progress...")
+        # Wait for download to complete
+        events = await monitor_task
+
+        # Try generation again
+        print(f"\n[{_timestamp()}] Retrying generation after download...")
+        await asyncio.sleep(2)
+        success, result = await trigger_generation(profile_id, test_text, model_size)
+
+        if success:
+            print("✓ Generation successful after download")
+
+        return events
+
+    # If model was already cached
+    await asyncio.sleep(3)
+    monitor_task.cancel()
+    try:
+        events = await monitor_task
+    except asyncio.CancelledError:
+        events = []
+
+    return events
+
+
+async def main():
+    print("=" * 80)
+    print("TTS Generation Progress Test")
+    print("=" * 80)
+    print("Purpose: Capture exact SSE events during generation to identify UX issues")
+    print("=" * 80)
+
+    # Check if server is running
+    print(f"\n[{_timestamp()}] Checking if server is running...")
+    if not await check_server():
+        print("✗ Server is not running on http://localhost:8000")
+        print("\nPlease start the server first:")
+        print("  cd backend && python main.py")
+        return False
+
+    print("✓ Server is running")
+
+    # Test Case 1: Cached model
+    print("\n" + "🧪 " * 20)
+    events_cached = await test_generation_with_cached_model()
+
+    # Results for Test Case 1
+    print("\n" + "=" * 80)
+    print("TEST CASE 1 RESULTS: Generation with Cached Model")
+    print("=" * 80)
+
+    if not events_cached:
+        print("✓ GOOD: No SSE progress events received")
+        print("  This is the expected behavior for a cached model.")
+    else:
+        print(f"⚠ ISSUE FOUND: Received {len(events_cached)} SSE events:")
+        print("\nEvent Timeline:")
+        for i, event in enumerate(events_cached, 1):
+            timestamp = event.pop("_timestamp", "??:??:??.???")
+            print(f"  {i}. [{timestamp}] {event}")
+
+        print("\n⚠ This explains the UX issue!")
+        print("  Users see progress events even when the model is already cached,")
+        print("  making them think the model is downloading again.")
+
+    print("\n" + "=" * 80)
+    print("Test Complete!")
+    print("=" * 80)
+
+    return True
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

backend/tests/test_offline_guard.py

@@ -0,0 +1,118 @@
+"""
+Unit tests for the ``force_offline_if_cached`` helper.
+
+Verifies that the helper mutates the cached module constants in
+``huggingface_hub.constants`` and ``transformers.utils.hub`` — not just
+``os.environ`` — and that concurrent users are refcount-coordinated so
+one thread's exit can't strip another thread's offline protection.
+
+NOTE: These tests mutate process-global state in ``huggingface_hub.constants``
+and ``transformers.utils.hub``. They are not safe under cross-process
+parallelism (e.g. ``pytest-xdist`` with ``--dist=loadfile``/``loadscope``);
+run this file serially.
+"""
+
+import os
+import sys
+import threading
+from pathlib import Path
+
+import pytest
+
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from utils.hf_offline_patch import force_offline_if_cached  # noqa: E402
+
+
+def _hf_const():
+    import huggingface_hub.constants as hf_const
+
+    return hf_const
+
+
+def _tf_hub():
+    import transformers.utils.hub as tf_hub
+
+    return tf_hub
+
+
+def test_mutates_cached_huggingface_hub_constant():
+    original = _hf_const().HF_HUB_OFFLINE
+    with force_offline_if_cached(True, "t"):
+        assert _hf_const().HF_HUB_OFFLINE is True
+    assert original == _hf_const().HF_HUB_OFFLINE
+
+
+def test_mutates_cached_transformers_constant():
+    original = _tf_hub()._is_offline_mode
+    with force_offline_if_cached(True, "t"):
+        assert _tf_hub()._is_offline_mode is True
+    assert original == _tf_hub()._is_offline_mode
+
+
+def test_sets_env_variable():
+    original = os.environ.get("HF_HUB_OFFLINE")
+    with force_offline_if_cached(True, "t"):
+        assert "1" == os.environ.get("HF_HUB_OFFLINE")
+    assert original == os.environ.get("HF_HUB_OFFLINE")
+
+
+def test_noop_when_not_cached():
+    before = _hf_const().HF_HUB_OFFLINE
+    with force_offline_if_cached(False, "t"):
+        assert before == _hf_const().HF_HUB_OFFLINE
+
+
+def test_nested_contexts_respect_refcount():
+    original = _hf_const().HF_HUB_OFFLINE
+    with force_offline_if_cached(True, "outer"):
+        assert _hf_const().HF_HUB_OFFLINE is True
+        with force_offline_if_cached(True, "inner"):
+            assert _hf_const().HF_HUB_OFFLINE is True
+        # inner exit must not restore while outer is still active
+        assert _hf_const().HF_HUB_OFFLINE is True
+    assert original == _hf_const().HF_HUB_OFFLINE
+
+
+def test_concurrent_threads_share_offline_window():
+    """A slow thread must keep seeing offline mode even if a peer exits first."""
+    original = _hf_const().HF_HUB_OFFLINE
+    observations: list[bool] = []
+    errors: list[Exception] = []
+    barrier = threading.Barrier(2)
+    fast_exited = threading.Event()
+
+    def slow():
+        try:
+            with force_offline_if_cached(True, "slow"):
+                barrier.wait(timeout=5)
+                assert fast_exited.wait(timeout=5), "fast thread did not exit"
+                observations.append(_hf_const().HF_HUB_OFFLINE)
+        except Exception as exc:  # noqa: BLE001
+            errors.append(exc)
+
+    def fast():
+        try:
+            with force_offline_if_cached(True, "fast"):
+                barrier.wait(timeout=5)
+        except Exception as exc:  # noqa: BLE001
+            errors.append(exc)
+        finally:
+            fast_exited.set()
+
+    t_slow = threading.Thread(target=slow)
+    t_fast = threading.Thread(target=fast)
+    t_slow.start()
+    t_fast.start()
+    t_slow.join(timeout=5)
+    t_fast.join(timeout=5)
+
+    assert not t_slow.is_alive(), "slow thread did not finish"
+    assert not t_fast.is_alive(), "fast thread did not finish"
+    assert not errors, errors
+    assert observations == [True], "slow thread lost offline protection"
+    assert original == _hf_const().HF_HUB_OFFLINE
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])

backend/tests/test_offline_patch.py

@@ -0,0 +1,113 @@
+"""
+Unit tests for ``patch_transformers_mistral_regex``.
+
+Verifies that our wrapper around
+``transformers.PreTrainedTokenizerBase._patch_mistral_regex`` catches
+exceptions from the unconditional ``huggingface_hub.model_info()`` lookup
+and returns the tokenizer unchanged — matching the success-path behavior
+for non-Mistral repos (transformers 4.57.3, ``tokenization_utils_base.py:2503``).
+
+NOTE: These tests mutate ``transformers.PreTrainedTokenizerBase`` globally;
+run serially, not under ``pytest-xdist`` with per-worker process isolation.
+"""
+
+import sys
+from pathlib import Path
+
+import pytest
+
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from huggingface_hub.errors import OfflineModeIsEnabled  # noqa: E402
+from transformers.tokenization_utils_base import PreTrainedTokenizerBase  # noqa: E402
+
+import utils.hf_offline_patch as hf_offline_patch  # noqa: E402
+
+
+@pytest.fixture(autouse=True)
+def restore_mistral_regex():
+    """Snapshot the current ``_patch_mistral_regex`` and restore after each test."""
+    saved = PreTrainedTokenizerBase.__dict__.get("_patch_mistral_regex")
+    saved_flag = hf_offline_patch._mistral_regex_patched
+    try:
+        yield
+    finally:
+        if saved is not None:
+            PreTrainedTokenizerBase._patch_mistral_regex = saved
+        hf_offline_patch._mistral_regex_patched = saved_flag
+
+
+def _apply_patch():
+    hf_offline_patch._mistral_regex_patched = False
+    hf_offline_patch.patch_transformers_mistral_regex()
+
+
+def test_suppresses_offline_mode_is_enabled(monkeypatch):
+    _apply_patch()
+
+    import huggingface_hub
+
+    def raise_offline(*_args, **_kwargs):
+        raise OfflineModeIsEnabled("offline")
+
+    monkeypatch.setattr(huggingface_hub, "model_info", raise_offline)
+
+    sentinel = object()
+    result = PreTrainedTokenizerBase._patch_mistral_regex(
+        sentinel, "Qwen/Qwen3-TTS-12Hz-1.7B-Base"
+    )
+    assert result is sentinel
+
+
+def test_suppresses_connection_errors(monkeypatch):
+    _apply_patch()
+
+    import huggingface_hub
+
+    def raise_connection(*_args, **_kwargs):
+        raise ConnectionError("network unreachable")
+
+    monkeypatch.setattr(huggingface_hub, "model_info", raise_connection)
+
+    sentinel = object()
+    result = PreTrainedTokenizerBase._patch_mistral_regex(
+        sentinel, "Qwen/Qwen3-TTS-12Hz-1.7B-Base"
+    )
+    assert result is sentinel
+
+
+def test_passthrough_on_success(monkeypatch):
+    """When model_info returns non-Mistral tags the original falls through and returns the tokenizer unchanged."""
+    _apply_patch()
+
+    import huggingface_hub
+
+    class FakeInfo:
+        tags = ["model-type:qwen", "language:en"]
+
+    monkeypatch.setattr(huggingface_hub, "model_info", lambda *_a, **_kw: FakeInfo())
+
+    sentinel = object()
+    result = PreTrainedTokenizerBase._patch_mistral_regex(
+        sentinel, "Qwen/Qwen3-TTS-12Hz-1.7B-Base"
+    )
+    assert result is sentinel
+
+
+def test_idempotent():
+    _apply_patch()
+    first = PreTrainedTokenizerBase._patch_mistral_regex
+    hf_offline_patch.patch_transformers_mistral_regex()
+    second = PreTrainedTokenizerBase._patch_mistral_regex
+    assert first.__func__ is second.__func__
+
+
+def test_missing_method_is_noop(monkeypatch):
+    monkeypatch.delattr(PreTrainedTokenizerBase, "_patch_mistral_regex", raising=False)
+    hf_offline_patch._mistral_regex_patched = False
+    hf_offline_patch.patch_transformers_mistral_regex()
+    assert hf_offline_patch._mistral_regex_patched is False
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])

backend/tests/test_profile_duplicate_names.py

@@ -0,0 +1,217 @@
+"""
+Tests for profile duplicate name validation.
+
+This test suite verifies that the application correctly handles
+duplicate profile names and provides user-friendly error messages.
+"""
+
+import pytest
+import tempfile
+import shutil
+from pathlib import Path
+from sqlalchemy import create_engine
+from sqlalchemy.orm import sessionmaker
+
+# Add parent directory to path to import backend modules
+import sys
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from database import Base, VoiceProfile as DBVoiceProfile
+from models import VoiceProfileCreate
+from profiles import create_profile, update_profile
+
+
+@pytest.fixture
+def test_db():
+    """Create a temporary test database."""
+    # Create temporary directory for test database
+    temp_dir = tempfile.mkdtemp()
+    db_path = Path(temp_dir) / "test.db"
+
+    # Create engine and session
+    engine = create_engine(f"sqlite:///{db_path}")
+    Base.metadata.create_all(bind=engine)
+    SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+
+    db = SessionLocal()
+
+    yield db
+
+    # Cleanup
+    db.close()
+    shutil.rmtree(temp_dir)
+
+
+@pytest.fixture
+def mock_profiles_dir(monkeypatch, tmp_path):
+    """Mock the profiles directory to use a temporary path."""
+    from backend import config
+    monkeypatch.setattr(config, 'get_profiles_dir', lambda: tmp_path)
+    return tmp_path
+
+
+@pytest.mark.asyncio
+async def test_create_profile_duplicate_name_raises_error(test_db, mock_profiles_dir):
+    """Test that creating a profile with a duplicate name raises a ValueError."""
+    # Create first profile
+    profile_data_1 = VoiceProfileCreate(
+        name="Test Profile",
+        description="First profile",
+        language="en"
+    )
+
+    profile_1 = await create_profile(profile_data_1, test_db)
+    assert profile_1.name == "Test Profile"
+
+    # Try to create second profile with same name
+    profile_data_2 = VoiceProfileCreate(
+        name="Test Profile",
+        description="Second profile",
+        language="en"
+    )
+
+    with pytest.raises(ValueError) as exc_info:
+        await create_profile(profile_data_2, test_db)
+
+    # Verify error message is user-friendly
+    assert "already exists" in str(exc_info.value)
+    assert "Test Profile" in str(exc_info.value)
+    assert "choose a different name" in str(exc_info.value).lower()
+
+
+@pytest.mark.asyncio
+async def test_create_profile_different_names_succeeds(test_db, mock_profiles_dir):
+    """Test that creating profiles with different names succeeds."""
+    # Create first profile
+    profile_data_1 = VoiceProfileCreate(
+        name="Profile One",
+        description="First profile",
+        language="en"
+    )
+
+    profile_1 = await create_profile(profile_data_1, test_db)
+    assert profile_1.name == "Profile One"
+
+    # Create second profile with different name
+    profile_data_2 = VoiceProfileCreate(
+        name="Profile Two",
+        description="Second profile",
+        language="en"
+    )
+
+    profile_2 = await create_profile(profile_data_2, test_db)
+    assert profile_2.name == "Profile Two"
+
+    # Verify both profiles exist
+    assert profile_1.id != profile_2.id
+
+
+@pytest.mark.asyncio
+async def test_update_profile_to_duplicate_name_raises_error(test_db, mock_profiles_dir):
+    """Test that updating a profile to a duplicate name raises a ValueError."""
+    # Create two profiles with different names
+    profile_data_1 = VoiceProfileCreate(
+        name="Profile A",
+        description="First profile",
+        language="en"
+    )
+    profile_1 = await create_profile(profile_data_1, test_db)
+
+    profile_data_2 = VoiceProfileCreate(
+        name="Profile B",
+        description="Second profile",
+        language="en"
+    )
+    profile_2 = await create_profile(profile_data_2, test_db)
+
+    # Try to update profile_2 to use profile_1's name
+    update_data = VoiceProfileCreate(
+        name="Profile A",  # Duplicate name
+        description="Updated description",
+        language="en"
+    )
+
+    with pytest.raises(ValueError) as exc_info:
+        await update_profile(profile_2.id, update_data, test_db)
+
+    # Verify error message is user-friendly
+    assert "already exists" in str(exc_info.value)
+    assert "Profile A" in str(exc_info.value)
+
+
+@pytest.mark.asyncio
+async def test_update_profile_keep_same_name_succeeds(test_db, mock_profiles_dir):
+    """Test that updating a profile while keeping the same name succeeds."""
+    # Create profile
+    profile_data = VoiceProfileCreate(
+        name="My Profile",
+        description="Original description",
+        language="en"
+    )
+    profile = await create_profile(profile_data, test_db)
+
+    # Update profile with same name but different description
+    update_data = VoiceProfileCreate(
+        name="My Profile",  # Same name
+        description="Updated description",
+        language="en"
+    )
+
+    updated_profile = await update_profile(profile.id, update_data, test_db)
+
+    # Verify update succeeded
+    assert updated_profile is not None
+    assert updated_profile.id == profile.id
+    assert updated_profile.name == "My Profile"
+    assert updated_profile.description == "Updated description"
+
+
+@pytest.mark.asyncio
+async def test_update_profile_to_new_unique_name_succeeds(test_db, mock_profiles_dir):
+    """Test that updating a profile to a new unique name succeeds."""
+    # Create profile
+    profile_data = VoiceProfileCreate(
+        name="Original Name",
+        description="Profile description",
+        language="en"
+    )
+    profile = await create_profile(profile_data, test_db)
+
+    # Update profile with new unique name
+    update_data = VoiceProfileCreate(
+        name="New Unique Name",
+        description="Updated description",
+        language="en"
+    )
+
+    updated_profile = await update_profile(profile.id, update_data, test_db)
+
+    # Verify update succeeded
+    assert updated_profile is not None
+    assert updated_profile.id == profile.id
+    assert updated_profile.name == "New Unique Name"
+
+
+@pytest.mark.asyncio
+async def test_case_sensitive_names_allowed(test_db, mock_profiles_dir):
+    """Test that profile names are case-sensitive (e.g., 'Test' and 'test' are different)."""
+    # Create profile with lowercase name
+    profile_data_1 = VoiceProfileCreate(
+        name="test profile",
+        description="Lowercase",
+        language="en"
+    )
+    profile_1 = await create_profile(profile_data_1, test_db)
+
+    # Create profile with different case
+    profile_data_2 = VoiceProfileCreate(
+        name="Test Profile",
+        description="Title case",
+        language="en"
+    )
+    profile_2 = await create_profile(profile_data_2, test_db)
+
+    # Both should succeed since SQLite unique constraint is case-sensitive by default
+    assert profile_1.name == "test profile"
+    assert profile_2.name == "Test Profile"
+    assert profile_1.id != profile_2.id

backend/tests/test_progress.py

@@ -0,0 +1,313 @@
+"""
+Test script to debug model download progress tracking.
+"""
+
+import asyncio
+import json
+import time
+from typing import List, Dict
+import logging
+
+# Set up logging to see what's happening
+logging.basicConfig(
+    level=logging.DEBUG,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+
+from utils.progress import ProgressManager, get_progress_manager
+from utils.hf_progress import HFProgressTracker, create_hf_progress_callback
+
+
+def test_progress_manager_basic():
+    """Test 1: Basic ProgressManager functionality."""
+    print("\n" + "=" * 60)
+    print("Test 1: ProgressManager Basic Operations")
+    print("=" * 60)
+
+    pm = ProgressManager()
+
+    # Test update_progress
+    pm.update_progress(
+        model_name="test-model",
+        current=50,
+        total=100,
+        filename="test.bin",
+        status="downloading"
+    )
+
+    # Test get_progress
+    progress = pm.get_progress("test-model")
+    print(f"✓ Progress stored: {progress}")
+    assert progress is not None
+    assert progress["progress"] == 50.0
+    assert progress["filename"] == "test.bin"
+    assert progress["status"] == "downloading"
+
+    # Test mark_complete
+    pm.mark_complete("test-model")
+    progress = pm.get_progress("test-model")
+    print(f"✓ Marked complete: {progress}")
+    assert progress["status"] == "complete"
+    assert progress["progress"] == 100.0
+
+    print("✓ Test 1 PASSED\n")
+    return True
+
+
+async def test_progress_manager_sse():
+    """Test 2: ProgressManager SSE streaming."""
+    print("\n" + "=" * 60)
+    print("Test 2: ProgressManager SSE Streaming")
+    print("=" * 60)
+
+    pm = ProgressManager()
+    collected_events: List[Dict] = []
+
+    # Simulate SSE client
+    async def sse_client():
+        """Simulates a frontend SSE connection."""
+        print("  SSE client: Subscribing to test-model-sse...")
+        async for event in pm.subscribe("test-model-sse"):
+            # Parse SSE event
+            if event.startswith("data: "):
+                data = json.loads(event[6:])
+                print(f"  SSE client: Received event: {data['status']} - {data.get('progress', 0):.1f}%")
+                collected_events.append(data)
+
+                # Stop when complete
+                if data.get("status") in ("complete", "error"):
+                    break
+            elif event.startswith(": heartbeat"):
+                print("  SSE client: Received heartbeat")
+
+    # Simulate download progress updates (from backend thread)
+    async def simulate_download():
+        """Simulates backend sending progress updates."""
+        print("  Backend: Starting simulated download...")
+        await asyncio.sleep(0.2)  # Let SSE client subscribe first
+
+        # Send progress updates
+        for i in range(0, 101, 20):
+            print(f"  Backend: Updating progress to {i}%")
+            pm.update_progress(
+                model_name="test-model-sse",
+                current=i,
+                total=100,
+                filename=f"file_{i}.bin",
+                status="downloading" if i < 100 else "downloading"
+            )
+            await asyncio.sleep(0.1)
+
+        # Mark complete
+        print("  Backend: Marking download complete")
+        pm.mark_complete("test-model-sse")
+
+    # Run SSE client and download simulation concurrently
+    await asyncio.gather(
+        sse_client(),
+        simulate_download()
+    )
+
+    # Verify we got events
+    print(f"\n  Collected {len(collected_events)} events")
+    assert len(collected_events) > 0, "Should have received at least one event"
+    assert collected_events[-1]["status"] == "complete", "Last event should be 'complete'"
+
+    print("✓ Test 2 PASSED\n")
+    return True
+
+
+def test_hf_progress_tracker():
+    """Test 3: HFProgressTracker tqdm patching."""
+    print("\n" + "=" * 60)
+    print("Test 3: HFProgressTracker tqdm Patching")
+    print("=" * 60)
+
+    captured_progress: List[tuple] = []
+
+    def progress_callback(downloaded: int, total: int, filename: str):
+        """Capture progress updates."""
+        captured_progress.append((downloaded, total, filename))
+        print(f"  Progress callback: {downloaded}/{total} bytes ({filename})")
+
+    tracker = HFProgressTracker(progress_callback)
+
+    # Simulate a download with tqdm
+    with tracker.patch_download():
+        try:
+            from tqdm import tqdm
+
+            # Simulate downloading a file
+            print("  Simulating download with tqdm...")
+            total_size = 1000
+            with tqdm(total=total_size, desc="model.bin", unit="B", unit_scale=True) as pbar:
+                for chunk in range(0, total_size, 100):
+                    pbar.update(100)
+                    time.sleep(0.01)
+
+            print(f"  Captured {len(captured_progress)} progress updates")
+            assert len(captured_progress) > 0, "Should have captured progress updates"
+
+            # Verify progress increases
+            last_downloaded = 0
+            for downloaded, total, filename in captured_progress:
+                assert downloaded >= last_downloaded, "Downloaded bytes should increase"
+                assert total == total_size, "Total should be consistent"
+                last_downloaded = downloaded
+
+            print("✓ Test 3 PASSED\n")
+            return True
+
+        except ImportError:
+            print("✗ tqdm not available, skipping test\n")
+            return None
+
+
+async def test_full_integration():
+    """Test 4: Full integration test."""
+    print("\n" + "=" * 60)
+    print("Test 4: Full Integration (ProgressManager + HFProgressTracker)")
+    print("=" * 60)
+
+    pm = get_progress_manager()
+    collected_events: List[Dict] = []
+
+    # SSE client
+    async def sse_client():
+        print("  SSE client: Subscribing...")
+        async for event in pm.subscribe("integration-test"):
+            if event.startswith("data: "):
+                data = json.loads(event[6:])
+                print(f"  SSE client: {data['status']} - {data.get('progress', 0):.1f}% - {data.get('filename', '')}")
+                collected_events.append(data)
+                if data.get("status") in ("complete", "error"):
+                    break
+
+    # Simulate backend download with HFProgressTracker
+    async def simulate_real_download():
+        await asyncio.sleep(0.2)  # Let SSE subscribe
+
+        print("  Backend: Starting download with HFProgressTracker...")
+
+        # Set up tracking (like the real backend does)
+        progress_callback = create_hf_progress_callback("integration-test", pm)
+        tracker = HFProgressTracker(progress_callback)
+
+        # Initialize progress
+        pm.update_progress(
+            model_name="integration-test",
+            current=0,
+            total=1,
+            filename="",
+            status="downloading"
+        )
+
+        # Simulate download with tqdm patching
+        with tracker.patch_download():
+            try:
+                from tqdm import tqdm
+
+                # Simulate multi-file download (like HuggingFace does)
+                files = [
+                    ("model.safetensors", 5000),
+                    ("config.json", 1000),
+                    ("tokenizer.json", 500),
+                ]
+
+                for filename, size in files:
+                    print(f"  Backend: Downloading {filename}...")
+                    with tqdm(total=size, desc=filename, unit="B") as pbar:
+                        for chunk in range(0, size, 500):
+                            chunk_size = min(500, size - chunk)
+                            pbar.update(chunk_size)
+                            await asyncio.sleep(0.05)
+
+                # Mark complete
+                print("  Backend: Download complete")
+                pm.mark_complete("integration-test")
+
+            except ImportError:
+                print("  ✗ tqdm not available")
+                pm.mark_error("integration-test", "tqdm not available")
+
+    # Run both
+    await asyncio.gather(
+        sse_client(),
+        simulate_real_download()
+    )
+
+    # Verify
+    print(f"\n  Collected {len(collected_events)} events")
+    if len(collected_events) > 0:
+        print(f"  First event: {collected_events[0]}")
+        print(f"  Last event: {collected_events[-1]}")
+        assert collected_events[-1]["status"] == "complete", "Should end with 'complete'"
+        print("✓ Test 4 PASSED\n")
+        return True
+    else:
+        print("✗ Test 4 FAILED - No events received\n")
+        return False
+
+
+async def main():
+    """Run all tests."""
+    print("\n" + "=" * 60)
+    print("Voicebox Progress Tracking Test Suite")
+    print("=" * 60)
+
+    results = []
+
+    # Test 1: Basic operations
+    try:
+        results.append(("Basic Operations", test_progress_manager_basic()))
+    except Exception as e:
+        print(f"✗ Test 1 FAILED: {e}\n")
+        results.append(("Basic Operations", False))
+
+    # Test 2: SSE streaming
+    try:
+        results.append(("SSE Streaming", await test_progress_manager_sse()))
+    except Exception as e:
+        print(f"✗ Test 2 FAILED: {e}\n")
+        results.append(("SSE Streaming", False))
+
+    # Test 3: tqdm patching
+    try:
+        results.append(("tqdm Patching", test_hf_progress_tracker()))
+    except Exception as e:
+        print(f"✗ Test 3 FAILED: {e}\n")
+        results.append(("tqdm Patching", False))
+
+    # Test 4: Full integration
+    try:
+        results.append(("Full Integration", await test_full_integration()))
+    except Exception as e:
+        print(f"✗ Test 4 FAILED: {e}\n")
+        results.append(("Full Integration", False))
+
+    # Summary
+    print("\n" + "=" * 60)
+    print("Test Results Summary")
+    print("=" * 60)
+
+    for name, result in results:
+        status = "✓ PASS" if result else ("⊘ SKIP" if result is None else "✗ FAIL")
+        print(f"  {status:8} {name}")
+
+    passed = sum(1 for _, r in results if r is True)
+    failed = sum(1 for _, r in results if r is False)
+    skipped = sum(1 for _, r in results if r is None)
+
+    print()
+    print(f"  Total: {len(results)} tests")
+    print(f"  Passed: {passed}")
+    print(f"  Failed: {failed}")
+    print(f"  Skipped: {skipped}")
+    print("=" * 60 + "\n")
+
+    return failed == 0
+
+
+if __name__ == "__main__":
+    success = asyncio.run(main())
+    exit(0 if success else 1)

backend/tests/test_qwen_download.py

@@ -0,0 +1,317 @@
+"""
+Test Qwen TTS model download with SSE progress monitoring.
+
+This specifically tests the MLX TTS backend download progress tracking,
+which requires tqdm to be patched BEFORE mlx_audio is imported.
+
+Usage:
+    cd backend && python -m tests.test_qwen_download
+
+Prerequisites:
+    - Server must be running: cd backend && python main.py
+    - Delete model first for fresh download test:
+      curl -X DELETE http://localhost:8000/models/qwen-tts-0.6B
+"""
+
+import asyncio
+import json
+import httpx
+import time
+from typing import List, Dict, Optional
+
+
+async def monitor_sse_stream(model_name: str, timeout: int = 600) -> List[Dict]:
+    """
+    Monitor SSE stream for a model download.
+    
+    Args:
+        model_name: Name of the model to monitor
+        timeout: Maximum time to wait for download (seconds)
+        
+    Returns:
+        List of SSE events received
+    """
+    events: List[Dict] = []
+    url = f"http://localhost:8000/models/progress/{model_name}"
+    last_progress = -1
+    
+    print(f"\n📡 Connecting to SSE endpoint: {url}")
+
+    try:
+        async with httpx.AsyncClient(timeout=timeout) as client:
+            async with client.stream("GET", url) as response:
+                print(f"   SSE connected, status: {response.status_code}")
+
+                if response.status_code != 200:
+                    print(f"   ❌ Error: SSE endpoint returned {response.status_code}")
+                    return events
+
+                async for line in response.aiter_lines():
+                    if not line:
+                        continue
+
+                    if line.startswith("data: "):
+                        try:
+                            data = json.loads(line[6:])
+                            events.append(data)
+                            
+                            # Print progress (only when it changes significantly)
+                            progress = data.get('progress', 0)
+                            status = data.get('status', 'unknown')
+                            filename = data.get('filename', '')
+                            current = data.get('current', 0)
+                            total = data.get('total', 0)
+                            
+                            # Print every 5% change or status change
+                            if abs(progress - last_progress) >= 5 or status in ('complete', 'error'):
+                                current_mb = current / (1024 * 1024)
+                                total_mb = total / (1024 * 1024)
+                                print(f"   📊 {status:12} {progress:6.1f}% ({current_mb:.1f}MB / {total_mb:.1f}MB) {filename[:50]}")
+                                last_progress = progress
+
+                            # Stop if complete or error
+                            if status in ("complete", "error"):
+                                if status == "complete":
+                                    print(f"   ✅ Download complete!")
+                                else:
+                                    print(f"   ❌ Download error: {data.get('error', 'unknown')}")
+                                break
+
+                        except json.JSONDecodeError as e:
+                            print(f"   ⚠️  Error parsing JSON: {e}")
+
+                    elif line.startswith(": heartbeat"):
+                        # Heartbeat every 1 second, don't spam
+                        pass
+
+    except asyncio.CancelledError:
+        print("   ⏹️  SSE monitor cancelled")
+    except Exception as e:
+        print(f"   ❌ SSE error: {e}")
+
+    return events
+
+
+async def trigger_download(model_name: str) -> bool:
+    """Trigger a model download via the API."""
+    url = "http://localhost:8000/models/download"
+
+    print(f"\n🚀 Triggering download for: {model_name}")
+
+    try:
+        async with httpx.AsyncClient(timeout=30) as client:
+            response = await client.post(url, json={"model_name": model_name})
+            result = response.json()
+            print(f"   Response: {response.status_code} - {result}")
+            return response.status_code == 200
+    except Exception as e:
+        print(f"   ❌ Error triggering download: {e}")
+        return False
+
+
+async def delete_model(model_name: str) -> bool:
+    """Delete a model from cache."""
+    url = f"http://localhost:8000/models/{model_name}"
+
+    print(f"\n🗑️  Deleting model: {model_name}")
+
+    try:
+        async with httpx.AsyncClient(timeout=30) as client:
+            response = await client.delete(url)
+            if response.status_code == 200:
+                print(f"   ✅ Model deleted")
+                return True
+            elif response.status_code == 404:
+                print(f"   ℹ️  Model not found (already deleted)")
+                return True
+            else:
+                print(f"   ⚠️  Delete response: {response.status_code} - {response.text}")
+                return False
+    except Exception as e:
+        print(f"   ❌ Error deleting model: {e}")
+        return False
+
+
+async def check_model_status(model_name: str) -> Optional[Dict]:
+    """Check the status of a model."""
+    try:
+        async with httpx.AsyncClient(timeout=10) as client:
+            response = await client.get("http://localhost:8000/models/status")
+            if response.status_code == 200:
+                data = response.json()
+                for model in data.get("models", []):
+                    if model["model_name"] == model_name:
+                        return model
+    except Exception as e:
+        print(f"   ⚠️  Error checking model status: {e}")
+    return None
+
+
+async def check_server() -> bool:
+    """Check if the server is running."""
+    try:
+        async with httpx.AsyncClient(timeout=5) as client:
+            response = await client.get("http://localhost:8000/health")
+            return response.status_code == 200
+    except Exception:
+        return False
+
+
+async def main():
+    print("=" * 70)
+    print("🧪 Qwen TTS Model Download Progress Test")
+    print("=" * 70)
+    print("\nThis test verifies that MLX TTS download progress tracking works.")
+    print("It specifically tests the tqdm patching for mlx_audio.tts imports.")
+
+    # Check if server is running
+    print("\n📡 Checking if server is running...")
+    if not await check_server():
+        print("   ❌ Server is not running on http://localhost:8000")
+        print("\n   Please start the server first:")
+        print("   cd backend && python main.py")
+        return False
+
+    print("   ✅ Server is running")
+
+    # Test model
+    model_name = "qwen-tts-0.6B"
+    
+    # Check current status
+    print(f"\n📊 Checking status of {model_name}...")
+    status = await check_model_status(model_name)
+    if status:
+        print(f"   Downloaded: {status.get('downloaded', False)}")
+        print(f"   Downloading: {status.get('downloading', False)}")
+        print(f"   Loaded: {status.get('loaded', False)}")
+        if status.get('size_mb'):
+            print(f"   Size: {status['size_mb']:.1f} MB")
+    else:
+        print("   ⚠️  Could not get model status")
+
+    # Ask if user wants to delete first
+    print("\n" + "-" * 70)
+    if status and status.get('downloaded'):
+        print("⚠️  Model is already downloaded. Delete it for a fresh download test?")
+        print("   [y] Yes, delete and download fresh")
+        print("   [n] No, just test SSE connection")
+        print("   [q] Quit")
+        
+        choice = input("\nChoice [y/n/q]: ").strip().lower()
+        
+        if choice == 'q':
+            print("Exiting...")
+            return True
+        
+        if choice == 'y':
+            if not await delete_model(model_name):
+                print("Failed to delete model. Continue anyway? [y/n]")
+                if input().strip().lower() != 'y':
+                    return False
+    else:
+        print("Model not downloaded. Will perform fresh download test.")
+        input("Press Enter to continue...")
+
+    # Run the test
+    print("\n" + "=" * 70)
+    print("🏃 Starting Download Test")
+    print("=" * 70)
+
+    async def run_test():
+        # Start SSE monitor in background FIRST
+        monitor_task = asyncio.create_task(monitor_sse_stream(model_name, timeout=600))
+
+        # Wait for SSE to connect
+        await asyncio.sleep(1)
+
+        # Trigger download
+        success = await trigger_download(model_name)
+
+        if not success:
+            print("   ❌ Failed to trigger download")
+            monitor_task.cancel()
+            try:
+                await monitor_task
+            except asyncio.CancelledError:
+                pass
+            return []
+
+        # Wait for SSE monitor to complete
+        print("\n⏳ Waiting for download to complete (this may take several minutes)...")
+        events = await monitor_task
+
+        return events
+
+    start_time = time.time()
+    events = await run_test()
+    elapsed = time.time() - start_time
+
+    # Results
+    print("\n" + "=" * 70)
+    print("📋 Test Results")
+    print("=" * 70)
+
+    print(f"\n⏱️  Elapsed time: {elapsed:.1f} seconds")
+    print(f"📨 Total SSE events received: {len(events)}")
+
+    if not events:
+        print("\n❌ FAILED - No SSE events received!")
+        print("\nPossible causes:")
+        print("  1. SSE endpoint not working")
+        print("  2. tqdm not patched before mlx_audio import")
+        print("  3. Progress callbacks not firing")
+        print("  4. Model already fully downloaded")
+        print("\nDebug steps:")
+        print("  1. Check server logs for [DEBUG] messages")
+        print("  2. Look for 'tqdm patched' before 'mlx_audio.tts import'")
+        print(f"  3. Delete model: curl -X DELETE http://localhost:8000/models/{model_name}")
+        return False
+
+    # Analyze events
+    first_event = events[0]
+    last_event = events[-1]
+    
+    print(f"\n📊 First event:")
+    print(f"   Status: {first_event.get('status')}")
+    print(f"   Progress: {first_event.get('progress', 0):.1f}%")
+    
+    print(f"\n📊 Last event:")
+    print(f"   Status: {last_event.get('status')}")
+    print(f"   Progress: {last_event.get('progress', 0):.1f}%")
+
+    # Check for expected behaviors
+    has_progress_updates = len(events) > 2
+    has_increasing_progress = False
+    has_complete = any(e.get('status') == 'complete' for e in events)
+    has_100_percent = any(e.get('progress', 0) >= 100 for e in events)
+    
+    # Check if progress increased over time
+    if len(events) >= 2:
+        progress_values = [e.get('progress', 0) for e in events]
+        has_increasing_progress = progress_values[-1] > progress_values[0]
+
+    print("\n📋 Checks:")
+    print(f"   {'✅' if has_progress_updates else '❌'} Multiple progress updates received ({len(events)} events)")
+    print(f"   {'✅' if has_increasing_progress else '❌'} Progress increased over time")
+    print(f"   {'✅' if has_100_percent else '❌'} Reached 100% progress")
+    print(f"   {'✅' if has_complete else '❌'} Received 'complete' status")
+
+    # Overall result
+    success = has_progress_updates and has_complete
+    
+    if success:
+        print("\n" + "=" * 70)
+        print("✅ TEST PASSED - Qwen TTS download progress tracking works!")
+        print("=" * 70)
+    else:
+        print("\n" + "=" * 70)
+        print("❌ TEST FAILED - Progress tracking has issues")
+        print("=" * 70)
+        print("\nCheck the server logs for debug output.")
+
+    return success
+
+
+if __name__ == "__main__":
+    result = asyncio.run(main())
+    exit(0 if result else 1)

backend/tests/test_task_queue_cancellation.py

@@ -0,0 +1,54 @@
+import asyncio
+
+import pytest
+
+from backend.services import task_queue
+
+
+@pytest.mark.asyncio
+async def test_cancel_queued_generation_skips_execution():
+    task_queue.init_queue(force=True)
+
+    running_started = asyncio.Event()
+    release_running = asyncio.Event()
+    queued_ran = asyncio.Event()
+
+    async def running_job():
+        running_started.set()
+        await release_running.wait()
+
+    async def queued_job():
+        queued_ran.set()
+
+    task_queue.enqueue_generation("gen-running", running_job())
+    await asyncio.wait_for(running_started.wait(), timeout=1)
+
+    task_queue.enqueue_generation("gen-queued", queued_job())
+    assert task_queue.cancel_generation("gen-queued") == "queued"
+
+    release_running.set()
+    await asyncio.sleep(0.1)
+
+    assert not queued_ran.is_set()
+
+
+@pytest.mark.asyncio
+async def test_cancel_running_generation_cancels_task():
+    task_queue.init_queue(force=True)
+
+    running_started = asyncio.Event()
+    running_cancelled = asyncio.Event()
+
+    async def running_job():
+        running_started.set()
+        try:
+            await asyncio.Event().wait()
+        except asyncio.CancelledError:
+            running_cancelled.set()
+            raise
+
+    task_queue.enqueue_generation("gen-running", running_job())
+    await asyncio.wait_for(running_started.wait(), timeout=1)
+
+    assert task_queue.cancel_generation("gen-running") == "running"
+    await asyncio.wait_for(running_cancelled.wait(), timeout=1)

backend/tests/test_whisper_download.py

@@ -0,0 +1,178 @@
+"""
+Test real model download with SSE progress monitoring.
+"""
+
+import asyncio
+import json
+import httpx
+import time
+from typing import List, Dict
+
+async def monitor_sse_stream(model_name: str, timeout: int = 300):
+    """Monitor SSE stream for a model download."""
+    events: List[Dict] = []
+    url = f"http://localhost:8000/models/progress/{model_name}"
+
+    print(f"Connecting to SSE endpoint: {url}")
+
+    async with httpx.AsyncClient(timeout=timeout) as client:
+        async with client.stream("GET", url) as response:
+            print(f"SSE connected, status: {response.status_code}")
+
+            if response.status_code != 200:
+                print(f"Error: SSE endpoint returned {response.status_code}")
+                return events
+
+            async for line in response.aiter_lines():
+                if not line:
+                    continue
+
+                print(f"  Raw SSE: {line[:100]}...")  # Print first 100 chars
+
+                if line.startswith("data: "):
+                    try:
+                        data = json.loads(line[6:])
+                        print(f"  → {data['status']:12} {data.get('progress', 0):6.1f}% {data.get('filename', '')}")
+                        events.append(data)
+
+                        # Stop if complete or error
+                        if data.get("status") in ("complete", "error"):
+                            print(f"  Download {data['status']}!")
+                            break
+
+                    except json.JSONDecodeError as e:
+                        print(f"  Error parsing JSON: {e}")
+                        print(f"  Line was: {line}")
+
+                elif line.startswith(": heartbeat"):
+                    print("  ♥ heartbeat")
+
+    return events
+
+
+async def trigger_download(model_name: str):
+    """Trigger a model download via the API."""
+    url = "http://localhost:8000/models/download"
+
+    print(f"\nTriggering download for: {model_name}")
+
+    async with httpx.AsyncClient(timeout=300) as client:
+        response = await client.post(url, json={"model_name": model_name})
+        print(f"Response: {response.status_code} - {response.json()}")
+        return response.status_code == 200
+
+
+async def check_server():
+    """Check if the server is running."""
+    try:
+        async with httpx.AsyncClient(timeout=5) as client:
+            response = await client.get("http://localhost:8000/health")
+            return response.status_code == 200
+    except Exception as e:
+        print(f"Server not running: {e}")
+        return False
+
+
+async def main():
+    print("=" * 60)
+    print("Real Model Download Progress Test")
+    print("=" * 60)
+
+    # Check if server is running
+    print("\nChecking if server is running...")
+    if not await check_server():
+        print("✗ Server is not running on http://localhost:8000")
+        print("\nPlease start the server first:")
+        print("  cd backend && python main.py")
+        return False
+
+    print("✓ Server is running")
+
+    # Choose a small model for testing
+    model_name = "whisper-base"  # ~150MB, faster to download
+    print(f"\nUsing model: {model_name}")
+
+    # Option to delete model first if it exists
+    print("\nDo you want to delete the model first to force a fresh download? (y/n)")
+    # For automated testing, skip deletion prompt
+    # delete_first = input().strip().lower() == 'y'
+    delete_first = False
+
+    if delete_first:
+        print(f"Deleting {model_name}...")
+        async with httpx.AsyncClient(timeout=30) as client:
+            response = await client.delete(f"http://localhost:8000/models/{model_name}")
+            print(f"Delete response: {response.status_code}")
+
+    print("\n" + "=" * 60)
+    print("Starting Test")
+    print("=" * 60)
+
+    # Start monitoring SSE stream BEFORE triggering download
+    async def run_test():
+        # Start SSE monitor in background
+        monitor_task = asyncio.create_task(monitor_sse_stream(model_name))
+
+        # Wait a bit to ensure SSE is connected
+        await asyncio.sleep(1)
+
+        # Trigger download
+        success = await trigger_download(model_name)
+
+        if not success:
+            print("✗ Failed to trigger download")
+            monitor_task.cancel()
+            return False
+
+        # Wait for SSE monitor to complete
+        events = await monitor_task
+
+        return events
+
+    events = await run_test()
+
+    # Results
+    print("\n" + "=" * 60)
+    print("Test Results")
+    print("=" * 60)
+
+    if not events:
+        print("✗ FAILED - No SSE events received!")
+        print("\nPossible causes:")
+        print("  1. SSE endpoint not working")
+        print("  2. Progress updates not being sent")
+        print("  3. Model already downloaded (no progress to report)")
+        print("\nTry deleting the model first to force a fresh download:")
+        print(f"  curl -X DELETE http://localhost:8000/models/{model_name}")
+        return False
+
+    print(f"✓ Received {len(events)} SSE events")
+    print(f"\nFirst event: {events[0]}")
+    print(f"Last event: {events[-1]}")
+
+    # Check if we got meaningful progress
+    has_progress = any(e.get('progress', 0) > 0 for e in events)
+    has_complete = any(e.get('status') == 'complete' for e in events)
+
+    if has_progress:
+        print("✓ Progress updates received")
+    else:
+        print("✗ No progress updates (might be already downloaded)")
+
+    if has_complete:
+        print("✓ Download completed successfully")
+    else:
+        print("✗ Download did not complete")
+
+    success = has_progress and has_complete
+
+    if success:
+        print("\n✓ TEST PASSED - Progress tracking works!")
+    else:
+        print("\n⊘ TEST INCONCLUSIVE - Try with a fresh download")
+
+    return success
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

backend/utils/__init__.py

@@ -0,0 +1 @@
+# Utils package

backend/utils/audio.py

@@ -0,0 +1,318 @@
+"""
+Audio processing utilities.
+"""
+
+import numpy as np
+import soundfile as sf
+import librosa
+from typing import Tuple, Optional
+
+
+def normalize_audio(
+    audio: np.ndarray,
+    target_db: float = -20.0,
+    peak_limit: float = 0.85,
+) -> np.ndarray:
+    """
+    Normalize audio to target loudness with peak limiting.
+    
+    Args:
+        audio: Input audio array
+        target_db: Target RMS level in dB
+        peak_limit: Peak limit (0.0-1.0)
+        
+    Returns:
+        Normalized audio array
+    """
+    # Convert to float32
+    audio = audio.astype(np.float32)
+    
+    # Calculate current RMS
+    rms = np.sqrt(np.mean(audio**2))
+    
+    # Calculate target RMS
+    target_rms = 10**(target_db / 20)
+    
+    # Apply gain
+    if rms > 0:
+        gain = target_rms / rms
+        audio = audio * gain
+    
+    # Peak limiting
+    audio = np.clip(audio, -peak_limit, peak_limit)
+    
+    return audio
+
+
+def load_audio(
+    path: str,
+    sample_rate: int = 24000,
+    mono: bool = True,
+) -> Tuple[np.ndarray, int]:
+    """
+    Load audio file with normalization.
+    
+    Args:
+        path: Path to audio file
+        sample_rate: Target sample rate
+        mono: Convert to mono
+        
+    Returns:
+        Tuple of (audio_array, sample_rate)
+    """
+    audio, sr = librosa.load(path, sr=sample_rate, mono=mono)
+    return audio, sr
+
+
+def save_audio(
+    audio: np.ndarray,
+    path: str,
+    sample_rate: int = 24000,
+) -> None:
+    """
+    Save audio file with atomic write and error handling.
+
+    Writes to a temporary file first, then atomically renames to the
+    target path.  This prevents corrupted/partial WAV files if the
+    process is interrupted mid-write.
+
+    Args:
+        audio: Audio array
+        path: Output path
+        sample_rate: Sample rate
+
+    Raises:
+        OSError: If file cannot be written
+    """
+    from pathlib import Path
+    import os
+
+    temp_path = f"{path}.tmp"
+    try:
+        # Ensure parent directory exists
+        Path(path).parent.mkdir(parents=True, exist_ok=True)
+
+        # Write to temporary file first (explicit format since .tmp
+        # extension is not recognised by soundfile)
+        sf.write(temp_path, audio, sample_rate, format='WAV')
+
+        # Atomic rename to final path
+        os.replace(temp_path, path)
+
+    except Exception as e:
+        # Clean up temp file on failure
+        try:
+            if Path(temp_path).exists():
+                Path(temp_path).unlink()
+        except Exception:
+            pass  # Best effort cleanup
+
+        raise OSError(f"Failed to save audio to {path}: {e}") from e
+
+
+def trim_tts_output(
+    audio: np.ndarray,
+    sample_rate: int = 24000,
+    frame_ms: int = 20,
+    silence_threshold_db: float = -40.0,
+    min_silence_ms: int = 200,
+    max_internal_silence_ms: int = 1000,
+    fade_ms: int = 30,
+) -> np.ndarray:
+    """
+    Trim trailing silence and post-silence hallucination from TTS output.
+
+    Chatterbox sometimes produces ``[speech][silence][hallucinated noise]``.
+    This detects internal silence gaps longer than *max_internal_silence_ms*
+    and cuts the audio at that boundary, then trims trailing silence and
+    applies a short cosine fade-out.
+
+    Args:
+        audio: Input audio array (mono float32)
+        sample_rate: Sample rate in Hz
+        frame_ms: Frame size for RMS energy calculation
+        silence_threshold_db: dB threshold below which a frame is silence
+        min_silence_ms: Minimum trailing silence to keep
+        max_internal_silence_ms: Cut after any silence gap longer than this
+        fade_ms: Cosine fade-out duration in ms
+
+    Returns:
+        Trimmed audio array
+    """
+    frame_len = int(sample_rate * frame_ms / 1000)
+    if frame_len == 0 or len(audio) < frame_len:
+        return audio
+
+    n_frames = len(audio) // frame_len
+    threshold_linear = 10 ** (silence_threshold_db / 20)
+
+    # Compute per-frame RMS
+    rms = np.array(
+        [
+            np.sqrt(np.mean(audio[i * frame_len : (i + 1) * frame_len] ** 2))
+            for i in range(n_frames)
+        ]
+    )
+    is_speech = rms >= threshold_linear
+
+    # Find first speech frame
+    first_speech = 0
+    for i, s in enumerate(is_speech):
+        if s:
+            first_speech = max(0, i - 1)  # keep 1 frame padding
+            break
+
+    # Walk forward from first speech; cut at long internal silence gaps
+    max_silence_frames = int(max_internal_silence_ms / frame_ms)
+    consecutive_silence = 0
+    cut_frame = n_frames
+
+    for i in range(first_speech, n_frames):
+        if is_speech[i]:
+            consecutive_silence = 0
+        else:
+            consecutive_silence += 1
+            if consecutive_silence >= max_silence_frames:
+                cut_frame = i - consecutive_silence + 1
+                break
+
+    # Trim trailing silence from the cut point
+    min_silence_frames = int(min_silence_ms / frame_ms)
+    end_frame = cut_frame
+    while end_frame > first_speech and not is_speech[end_frame - 1]:
+        end_frame -= 1
+    # Keep a short tail
+    end_frame = min(end_frame + min_silence_frames, cut_frame)
+
+    # Convert frames back to samples
+    start_sample = first_speech * frame_len
+    end_sample = min(end_frame * frame_len, len(audio))
+
+    trimmed = audio[start_sample:end_sample].copy()
+
+    # Cosine fade-out
+    fade_samples = int(sample_rate * fade_ms / 1000)
+    if fade_samples > 0 and len(trimmed) > fade_samples:
+        fade = np.cos(np.linspace(0, np.pi / 2, fade_samples)) ** 2
+        trimmed[-fade_samples:] *= fade
+
+    return trimmed
+
+
+def preprocess_reference_audio(
+    audio: np.ndarray,
+    sample_rate: int,
+    peak_target: float = 0.95,
+    trim_top_db: float = 40.0,
+    edge_padding_ms: int = 100,
+) -> np.ndarray:
+    """
+    Clean up a reference-audio sample before validation/storage.
+
+    Removes DC offset, trims leading/trailing silence, and caps the peak so a
+    slightly-hot recording doesn't get rejected downstream as "clipping". The
+    goal is to accept reasonable real-world recordings — not to repair badly
+    distorted ones. True clipping artifacts inside the waveform can't be
+    recovered by peak scaling and will still sound bad.
+
+    Args:
+        audio: Mono audio array.
+        sample_rate: Sample rate of ``audio`` in Hz.
+        peak_target: Peak amplitude cap in [0, 1]. Applied only if the input
+            peak exceeds this value.
+        trim_top_db: Silence threshold for edge trimming, in dB below peak.
+            40 dB sits below normal speech dynamic range (≈30 dB) so soft
+            trailing syllables are preserved, while still catching obvious
+            leading/trailing silence. Lower values are more aggressive;
+            librosa's own default is 60.
+        edge_padding_ms: Milliseconds of padding to add back at each edge
+            *only if* trimming shortened the waveform, so TTS engines have a
+            brief silence to anchor on without ever making the output longer
+            than the input.
+
+    Returns:
+        Preprocessed audio array (float32).
+    """
+    audio = audio.astype(np.float32, copy=False)
+
+    if audio.size == 0:
+        return audio
+
+    audio = audio - float(np.mean(audio))
+
+    trimmed, _ = librosa.effects.trim(audio, top_db=trim_top_db)
+    if 0 < trimmed.size < audio.size:
+        pad_each = int(sample_rate * edge_padding_ms / 1000)
+        # Never pad past the original length — for near-max-duration uploads
+        # an unconditional pad would push them over the 30 s ceiling and
+        # trigger a spurious "too long" rejection.
+        headroom = (audio.size - trimmed.size) // 2
+        pad = min(pad_each, max(headroom, 0))
+        if pad > 0:
+            trimmed = np.pad(trimmed, (pad, pad), mode="constant")
+        audio = trimmed
+
+    peak = float(np.abs(audio).max())
+    if peak > peak_target and peak > 0:
+        audio = audio * (peak_target / peak)
+
+    return audio
+
+
+def validate_reference_audio(
+    audio_path: str,
+    min_duration: float = 2.0,
+    max_duration: float = 30.0,
+    min_rms: float = 0.01,
+) -> Tuple[bool, Optional[str]]:
+    """
+    Validate reference audio for voice cloning.
+
+    Args:
+        audio_path: Path to audio file
+        min_duration: Minimum duration in seconds
+        max_duration: Maximum duration in seconds
+        min_rms: Minimum RMS level
+
+    Returns:
+        Tuple of (is_valid, error_message)
+    """
+    result = validate_and_load_reference_audio(
+        audio_path, min_duration, max_duration, min_rms
+    )
+    return (result[0], result[1])
+
+
+def validate_and_load_reference_audio(
+    audio_path: str,
+    min_duration: float = 2.0,
+    max_duration: float = 30.0,
+    min_rms: float = 0.01,
+) -> Tuple[bool, Optional[str], Optional[np.ndarray], Optional[int]]:
+    """
+    Validate and load reference audio in a single pass.
+
+    Applies :func:`preprocess_reference_audio` before checks so that
+    slightly-hot recordings aren't rejected as clipping. Duration and RMS
+    checks run on the preprocessed waveform.
+
+    Returns:
+        Tuple of (is_valid, error_message, audio_array, sample_rate)
+    """
+    try:
+        audio, sr = load_audio(audio_path)
+        audio = preprocess_reference_audio(audio, sr)
+        duration = len(audio) / sr
+
+        if duration < min_duration:
+            return False, f"Audio too short (minimum {min_duration} seconds)", None, None
+        if duration > max_duration:
+            return False, f"Audio too long (maximum {max_duration} seconds)", None, None
+
+        rms = np.sqrt(np.mean(audio**2))
+        if rms < min_rms:
+            return False, "Audio is too quiet or silent", None, None
+
+        return True, None, audio, sr
+    except Exception as e:
+        return False, f"Error validating audio: {str(e)}", None, None

backend/utils/cache.py

@@ -0,0 +1,153 @@
+"""
+Voice prompt caching utilities.
+"""
+
+import hashlib
+import logging
+import torch
+from pathlib import Path
+from typing import Optional, Union, Dict, Any
+
+from .. import config
+
+logger = logging.getLogger(__name__)
+
+
+def _get_cache_dir() -> Path:
+    """Get cache directory from config."""
+    return config.get_cache_dir()
+
+
+# In-memory cache - can store dict (voice prompt) or tensor (legacy)
+_memory_cache: dict[str, Union[torch.Tensor, Dict[str, Any]]] = {}
+
+
+def get_cache_key(audio_path: str, reference_text: str) -> str:
+    """
+    Generate cache key from audio file and reference text.
+
+    Args:
+        audio_path: Path to audio file
+        reference_text: Reference text
+
+    Returns:
+        Cache key (MD5 hash)
+    """
+    # Read audio file
+    with open(audio_path, "rb") as f:
+        audio_bytes = f.read()
+
+    # Combine audio bytes and text
+    combined = audio_bytes + reference_text.encode("utf-8")
+
+    # Generate hash
+    return hashlib.md5(combined).hexdigest()
+
+
+def get_cached_voice_prompt(
+    cache_key: str,
+) -> Optional[Union[torch.Tensor, Dict[str, Any]]]:
+    """
+    Get cached voice prompt if available.
+
+    Args:
+        cache_key: Cache key
+
+    Returns:
+        Cached voice prompt (dict or tensor) or None
+    """
+    # Check in-memory cache
+    if cache_key in _memory_cache:
+        return _memory_cache[cache_key]
+
+    # Check disk cache
+    cache_file = _get_cache_dir() / f"{cache_key}.prompt"
+    if cache_file.exists():
+        try:
+            prompt = torch.load(cache_file, weights_only=True)
+            _memory_cache[cache_key] = prompt
+            return prompt
+        except Exception:
+            # Cache file corrupted, delete it
+            cache_file.unlink()
+
+    return None
+
+
+def cache_voice_prompt(
+    cache_key: str,
+    voice_prompt: Union[torch.Tensor, Dict[str, Any]],
+) -> None:
+    """
+    Cache voice prompt to memory and disk.
+
+    Args:
+        cache_key: Cache key
+        voice_prompt: Voice prompt (dict or tensor)
+    """
+    # Store in memory
+    _memory_cache[cache_key] = voice_prompt
+
+    # Store on disk (torch.save can handle both dicts and tensors)
+    cache_file = _get_cache_dir() / f"{cache_key}.prompt"
+    torch.save(voice_prompt, cache_file)
+
+
+def clear_voice_prompt_cache() -> int:
+    """
+    Clear all voice prompt caches (memory and disk).
+
+    Returns:
+        Number of cache files deleted
+    """
+    # Clear memory cache
+    _memory_cache.clear()
+
+    # Clear disk cache
+    cache_dir = _get_cache_dir()
+    deleted_count = 0
+
+    if cache_dir.exists():
+        # Delete prompt cache files
+        for cache_file in cache_dir.glob("*.prompt"):
+            try:
+                cache_file.unlink()
+                deleted_count += 1
+            except Exception as e:
+                logger.warning("Failed to delete cache file %s: %s", cache_file, e)
+
+        # Delete combined audio files
+        for audio_file in cache_dir.glob("combined_*.wav"):
+            try:
+                audio_file.unlink()
+                deleted_count += 1
+            except Exception as e:
+                logger.warning("Failed to delete combined audio file %s: %s", audio_file, e)
+
+    return deleted_count
+
+
+def clear_profile_cache(profile_id: str) -> int:
+    """
+    Clear cache files for a specific profile.
+
+    Args:
+        profile_id: Profile ID
+
+    Returns:
+        Number of cache files deleted
+    """
+    cache_dir = _get_cache_dir()
+    deleted_count = 0
+
+    if cache_dir.exists():
+        # Delete combined audio files for this profile
+        pattern = f"combined_{profile_id}_*.wav"
+        for audio_file in cache_dir.glob(pattern):
+            try:
+                audio_file.unlink()
+                deleted_count += 1
+            except Exception as e:
+                logger.warning("Failed to delete combined audio file %s: %s", audio_file, e)
+
+    return deleted_count

backend/utils/chunked_tts.py

@@ -0,0 +1,299 @@
+"""
+Chunked TTS generation utilities.
+
+Splits long text into sentence-boundary chunks, generates audio per-chunk
+via any TTSBackend, and concatenates with crossfade.  All logic is
+engine-agnostic — it wraps the standard ``TTSBackend.generate()`` interface.
+
+Short text (≤ max_chunk_chars) uses the single-shot fast path with zero
+overhead.
+"""
+
+import logging
+import re
+from typing import List, Tuple
+
+import numpy as np
+
+logger = logging.getLogger("voicebox.chunked-tts")
+
+# Default chunk size in characters.  Can be overridden per-request via
+# the ``max_chunk_chars`` field on GenerationRequest.
+DEFAULT_MAX_CHUNK_CHARS = 800
+
+# Common abbreviations that should NOT be treated as sentence endings.
+# Lowercase for case-insensitive matching.
+_ABBREVIATIONS = frozenset(
+    {
+        "mr",
+        "mrs",
+        "ms",
+        "dr",
+        "prof",
+        "sr",
+        "jr",
+        "st",
+        "ave",
+        "blvd",
+        "inc",
+        "ltd",
+        "corp",
+        "dept",
+        "est",
+        "approx",
+        "vs",
+        "etc",
+        "e.g",
+        "i.e",
+        "a.m",
+        "p.m",
+        "u.s",
+        "u.s.a",
+        "u.k",
+    }
+)
+
+# Paralinguistic tags used by Chatterbox Turbo.  The splitter must never
+# cut inside one of these.
+_PARA_TAG_RE = re.compile(r"\[[^\]]*\]")
+
+
+def split_text_into_chunks(text: str, max_chars: int = DEFAULT_MAX_CHUNK_CHARS) -> List[str]:
+    """Split *text* at natural boundaries into chunks of at most *max_chars*.
+
+    Priority: sentence-end (``.!?`` not preceded by an abbreviation and not
+    inside brackets) → clause boundary (``;:,—``) → whitespace → hard cut.
+
+    Paralinguistic tags like ``[laugh]`` are treated as atomic and will not
+    be split across chunks.
+    """
+    text = text.strip()
+    if not text:
+        return []
+    if len(text) <= max_chars:
+        return [text]
+
+    chunks: List[str] = []
+    remaining = text
+
+    while remaining:
+        remaining = remaining.lstrip()
+        if not remaining:
+            break
+        if len(remaining) <= max_chars:
+            chunks.append(remaining)
+            break
+
+        segment = remaining[:max_chars]
+
+        # Try to split at the last real sentence ending
+        split_pos = _find_last_sentence_end(segment)
+        if split_pos == -1:
+            split_pos = _find_last_clause_boundary(segment)
+        if split_pos == -1:
+            split_pos = segment.rfind(" ")
+        if split_pos == -1:
+            # Absolute fallback: hard cut but avoid splitting inside a tag
+            split_pos = _safe_hard_cut(segment, max_chars)
+
+        chunk = remaining[: split_pos + 1].strip()
+        if chunk:
+            chunks.append(chunk)
+        remaining = remaining[split_pos + 1 :]
+
+    return chunks
+
+
+def _find_last_sentence_end(text: str) -> int:
+    """Return the index of the last sentence-ending punctuation in *text*.
+
+    Skips periods that follow common abbreviations (``Dr.``, ``Mr.``, etc.)
+    and periods inside bracket tags (``[laugh]``).  Also handles CJK
+    sentence-ending punctuation (``。！？``).
+    """
+    best = -1
+    # ASCII sentence ends
+    for m in re.finditer(r"[.!?](?:\s|$)", text):
+        pos = m.start()
+        char = text[pos]
+        # Skip periods after abbreviations
+        if char == ".":
+            # Walk backwards to find the preceding word
+            word_start = pos - 1
+            while word_start >= 0 and text[word_start].isalpha():
+                word_start -= 1
+            word = text[word_start + 1 : pos].lower()
+            if word in _ABBREVIATIONS:
+                continue
+            # Skip decimal numbers (digit immediately before the period)
+            if word_start >= 0 and text[word_start].isdigit():
+                continue
+        # Skip if we're inside a bracket tag
+        if _inside_bracket_tag(text, pos):
+            continue
+        best = pos
+    # CJK sentence-ending punctuation
+    for m in re.finditer(r"[\u3002\uff01\uff1f]", text):
+        if m.start() > best:
+            best = m.start()
+    return best
+
+
+def _find_last_clause_boundary(text: str) -> int:
+    """Return the index of the last clause-boundary punctuation."""
+    best = -1
+    for m in re.finditer(r"[;:,\u2014](?:\s|$)", text):
+        pos = m.start()
+        # Skip if inside a bracket tag
+        if _inside_bracket_tag(text, pos):
+            continue
+        best = pos
+    return best
+
+
+def _inside_bracket_tag(text: str, pos: int) -> bool:
+    """Return True if *pos* falls inside a ``[...]`` tag."""
+    for m in _PARA_TAG_RE.finditer(text):
+        if m.start() < pos < m.end():
+            return True
+    return False
+
+
+def _safe_hard_cut(segment: str, max_chars: int) -> int:
+    """Find a hard-cut position that doesn't split a ``[tag]``."""
+    cut = max_chars - 1
+    # Check if the cut falls inside a bracket tag; if so, move before it
+    for m in _PARA_TAG_RE.finditer(segment):
+        if m.start() < cut < m.end():
+            return m.start() - 1 if m.start() > 0 else cut
+    return cut
+
+
+def concatenate_audio_chunks(
+    chunks: List[np.ndarray],
+    sample_rate: int,
+    crossfade_ms: int = 50,
+) -> np.ndarray:
+    """Concatenate audio arrays with a short crossfade to eliminate clicks.
+
+    Each chunk is expected to be a 1-D float32 ndarray at *sample_rate* Hz.
+    """
+    if not chunks:
+        return np.array([], dtype=np.float32)
+    if len(chunks) == 1:
+        return chunks[0]
+
+    crossfade_samples = int(sample_rate * crossfade_ms / 1000)
+    result = np.array(chunks[0], dtype=np.float32, copy=True)
+
+    for chunk in chunks[1:]:
+        if len(chunk) == 0:
+            continue
+        overlap = min(crossfade_samples, len(result), len(chunk))
+        if overlap > 0:
+            fade_out = np.linspace(1.0, 0.0, overlap, dtype=np.float32)
+            fade_in = np.linspace(0.0, 1.0, overlap, dtype=np.float32)
+            result[-overlap:] = result[-overlap:] * fade_out + chunk[:overlap] * fade_in
+            result = np.concatenate([result, chunk[overlap:]])
+        else:
+            result = np.concatenate([result, chunk])
+
+    return result
+
+
+async def generate_chunked(
+    backend,
+    text: str,
+    voice_prompt: dict,
+    language: str = "en",
+    seed: int | None = None,
+    instruct: str | None = None,
+    max_chunk_chars: int = DEFAULT_MAX_CHUNK_CHARS,
+    crossfade_ms: int = 50,
+    trim_fn=None,
+) -> Tuple[np.ndarray, int]:
+    """Generate audio with automatic chunking for long text.
+
+    For text shorter than *max_chunk_chars* this is a thin wrapper around
+    ``backend.generate()`` with zero overhead.
+
+    For longer text the input is split at natural sentence boundaries,
+    each chunk is generated independently, optionally trimmed (useful for
+    Chatterbox engines that hallucinate trailing noise), and the results
+    are concatenated with a crossfade (or hard cut if *crossfade_ms* is 0).
+
+    Parameters
+    ----------
+    backend : TTSBackend
+        Any backend implementing the ``generate()`` protocol.
+    text : str
+        Input text (may be arbitrarily long).
+    voice_prompt, language, seed, instruct
+        Forwarded to ``backend.generate()`` verbatim.
+    max_chunk_chars : int
+        Maximum characters per chunk (default 800).
+    crossfade_ms : int
+        Crossfade duration in milliseconds between chunks.  0 for a hard
+        cut with no overlap (default 50).
+    trim_fn : callable | None
+        Optional ``(audio, sample_rate) -> audio`` post-processing
+        function applied to each chunk before concatenation (e.g.
+        ``trim_tts_output`` for Chatterbox engines).
+
+    Returns
+    -------
+    (audio, sample_rate) : Tuple[np.ndarray, int]
+    """
+    chunks = split_text_into_chunks(text, max_chunk_chars)
+
+    if len(chunks) <= 1:
+        # Short text — single-shot fast path
+        audio, sample_rate = await backend.generate(
+            text,
+            voice_prompt,
+            language,
+            seed,
+            instruct,
+        )
+        if trim_fn is not None:
+            audio = trim_fn(audio, sample_rate)
+        return audio, sample_rate
+
+    # Long text — chunked generation
+    logger.info(
+        "Splitting %d chars into %d chunks (max %d chars each)",
+        len(text),
+        len(chunks),
+        max_chunk_chars,
+    )
+    audio_chunks: List[np.ndarray] = []
+    sample_rate: int | None = None
+
+    for i, chunk_text in enumerate(chunks):
+        logger.info(
+            "Generating chunk %d/%d (%d chars)",
+            i + 1,
+            len(chunks),
+            len(chunk_text),
+        )
+        # Vary the seed per chunk to avoid correlated RNG artefacts,
+        # but keep it deterministic so the same (text, seed) pair
+        # always produces the same output.
+        chunk_seed = (seed + i) if seed is not None else None
+
+        chunk_audio, chunk_sr = await backend.generate(
+            chunk_text,
+            voice_prompt,
+            language,
+            chunk_seed,
+            instruct,
+        )
+        if trim_fn is not None:
+            chunk_audio = trim_fn(chunk_audio, chunk_sr)
+
+        audio_chunks.append(np.asarray(chunk_audio, dtype=np.float32))
+        if sample_rate is None:
+            sample_rate = chunk_sr
+
+    audio = concatenate_audio_chunks(audio_chunks, sample_rate, crossfade_ms=crossfade_ms)
+    return audio, sample_rate

backend/utils/dac_shim.py

@@ -0,0 +1,95 @@
+"""
+Minimal shim for descript-audio-codec (DAC).
+
+TADA only imports Snake1d from dac.nn.layers and dac.model.dac.
+The real DAC package pulls in descript-audiotools which depends on
+onnx, tensorboard, protobuf, matplotlib, pystoi, etc. — none of
+which are needed for TADA's runtime use of Snake1d.
+
+This shim provides the exact Snake1d implementation (MIT-licensed,
+from https://github.com/descriptinc/descript-audio-codec) so we can
+avoid the entire audiotools dependency chain.
+
+If the real DAC package is installed, this module is never used —
+Python's import system will find the site-packages version first.
+Install this shim only when descript-audio-codec is NOT installed.
+"""
+
+import sys
+import types
+
+import torch
+import torch.nn as nn
+
+
+# ── Snake activation (from dac/nn/layers.py) ────────────────────────
+
+# NOTE: The original DAC code uses @torch.jit.script here for a 1.4x
+# speedup.  We omit it because TorchScript calls inspect.getsource()
+# which fails inside a PyInstaller frozen binary (no .py source files).
+def snake(x: torch.Tensor, alpha: torch.Tensor) -> torch.Tensor:
+    shape = x.shape
+    x = x.reshape(shape[0], shape[1], -1)
+    x = x + (alpha + 1e-9).reciprocal() * torch.sin(alpha * x).pow(2)
+    x = x.reshape(shape)
+    return x
+
+
+class Snake1d(nn.Module):
+    def __init__(self, channels: int):
+        super().__init__()
+        self.alpha = nn.Parameter(torch.ones(1, channels, 1))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return snake(x, self.alpha)
+
+
+# ── Register as dac.nn.layers and dac.model.dac ─────────────────────
+
+def install_dac_shim() -> None:
+    """Register fake dac package modules in sys.modules.
+
+    Only installs the shim if 'dac' is not already importable
+    (i.e. the real descript-audio-codec is not installed).
+    """
+    try:
+        import dac  # noqa: F401  — real package exists, do nothing
+        return
+    except ImportError:
+        pass
+
+    # Create the module tree: dac -> dac.nn -> dac.nn.layers
+    #                              -> dac.model -> dac.model.dac
+    dac_pkg = types.ModuleType("dac")
+    dac_pkg.__path__ = []  # make it a package
+    dac_pkg.__package__ = "dac"
+
+    dac_nn = types.ModuleType("dac.nn")
+    dac_nn.__path__ = []
+    dac_nn.__package__ = "dac.nn"
+
+    dac_nn_layers = types.ModuleType("dac.nn.layers")
+    dac_nn_layers.__package__ = "dac.nn"
+    dac_nn_layers.Snake1d = Snake1d
+    dac_nn_layers.snake = snake
+
+    dac_model = types.ModuleType("dac.model")
+    dac_model.__path__ = []
+    dac_model.__package__ = "dac.model"
+
+    dac_model_dac = types.ModuleType("dac.model.dac")
+    dac_model_dac.__package__ = "dac.model"
+    dac_model_dac.Snake1d = Snake1d
+
+    # Wire up submodules
+    dac_pkg.nn = dac_nn
+    dac_pkg.model = dac_model
+    dac_nn.layers = dac_nn_layers
+    dac_model.dac = dac_model_dac
+
+    # Register in sys.modules
+    sys.modules["dac"] = dac_pkg
+    sys.modules["dac.nn"] = dac_nn
+    sys.modules["dac.nn.layers"] = dac_nn_layers
+    sys.modules["dac.model"] = dac_model
+    sys.modules["dac.model.dac"] = dac_model_dac

backend/utils/effects.py

@@ -0,0 +1,373 @@
+"""
+Audio post-processing effects engine.
+
+Uses Spotify's pedalboard library to apply professional-grade DSP effects
+to generated audio. Effects are described as a JSON-serializable chain
+(list of effect dicts) so they can be stored in the database and sent
+over the API.
+
+Supported effect types:
+  - chorus      (flanger-style with short delays)
+  - reverb      (room reverb)
+  - delay       (echo / delay line)
+  - compressor  (dynamic range compression)
+  - gain        (volume adjustment in dB)
+  - highpass     (high-pass filter)
+  - lowpass      (low-pass filter)
+  - pitch_shift (semitone pitch shifting)
+"""
+
+from __future__ import annotations
+
+import numpy as np
+from typing import Any, Dict, List, Optional
+
+from pedalboard import (
+    Pedalboard,
+    Chorus,
+    Reverb,
+    Compressor,
+    Gain,
+    HighpassFilter,
+    LowpassFilter,
+    Delay,
+    PitchShift,
+)
+
+
+# Each param definition: (default, min, max, description)
+EFFECT_REGISTRY: Dict[str, Dict[str, Any]] = {
+    "chorus": {
+        "cls": Chorus,
+        "label": "Chorus / Flanger",
+        "description": "Modulated delay for flanging or chorus effects. Short centre_delay_ms (<10) gives flanger; longer gives chorus.",
+        "params": {
+            "rate_hz": {"default": 1.0, "min": 0.01, "max": 20.0, "step": 0.01, "description": "LFO speed (Hz)"},
+            "depth": {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "description": "Modulation depth"},
+            "feedback": {"default": 0.0, "min": 0.0, "max": 0.95, "step": 0.01, "description": "Feedback amount"},
+            "centre_delay_ms": {
+                "default": 7.0,
+                "min": 0.5,
+                "max": 50.0,
+                "step": 0.1,
+                "description": "Centre delay (ms)",
+            },
+            "mix": {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "description": "Wet/dry mix"},
+        },
+    },
+    "reverb": {
+        "cls": Reverb,
+        "label": "Reverb",
+        "description": "Room reverb effect.",
+        "params": {
+            "room_size": {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "description": "Room size"},
+            "damping": {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "description": "High frequency damping"},
+            "wet_level": {"default": 0.33, "min": 0.0, "max": 1.0, "step": 0.01, "description": "Wet level"},
+            "dry_level": {"default": 0.4, "min": 0.0, "max": 1.0, "step": 0.01, "description": "Dry level"},
+            "width": {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "description": "Stereo width"},
+        },
+    },
+    "delay": {
+        "cls": Delay,
+        "label": "Delay",
+        "description": "Echo / delay line.",
+        "params": {
+            "delay_seconds": {
+                "default": 0.3,
+                "min": 0.01,
+                "max": 2.0,
+                "step": 0.01,
+                "description": "Delay time (seconds)",
+            },
+            "feedback": {"default": 0.3, "min": 0.0, "max": 0.95, "step": 0.01, "description": "Feedback amount"},
+            "mix": {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01, "description": "Wet/dry mix"},
+        },
+    },
+    "compressor": {
+        "cls": Compressor,
+        "label": "Compressor",
+        "description": "Dynamic range compression for consistent loudness.",
+        "params": {
+            "threshold_db": {"default": -20.0, "min": -60.0, "max": 0.0, "step": 0.5, "description": "Threshold (dB)"},
+            "ratio": {"default": 4.0, "min": 1.0, "max": 20.0, "step": 0.1, "description": "Compression ratio"},
+            "attack_ms": {"default": 10.0, "min": 0.1, "max": 100.0, "step": 0.1, "description": "Attack time (ms)"},
+            "release_ms": {
+                "default": 100.0,
+                "min": 10.0,
+                "max": 1000.0,
+                "step": 1.0,
+                "description": "Release time (ms)",
+            },
+        },
+    },
+    "gain": {
+        "cls": Gain,
+        "label": "Gain",
+        "description": "Volume adjustment in decibels.",
+        "params": {
+            "gain_db": {"default": 0.0, "min": -40.0, "max": 40.0, "step": 0.5, "description": "Gain (dB)"},
+        },
+    },
+    "highpass": {
+        "cls": HighpassFilter,
+        "label": "High-Pass Filter",
+        "description": "Removes frequencies below the cutoff.",
+        "params": {
+            "cutoff_frequency_hz": {
+                "default": 80.0,
+                "min": 20.0,
+                "max": 8000.0,
+                "step": 1.0,
+                "description": "Cutoff frequency (Hz)",
+            },
+        },
+    },
+    "lowpass": {
+        "cls": LowpassFilter,
+        "label": "Low-Pass Filter",
+        "description": "Removes frequencies above the cutoff.",
+        "params": {
+            "cutoff_frequency_hz": {
+                "default": 8000.0,
+                "min": 200.0,
+                "max": 20000.0,
+                "step": 1.0,
+                "description": "Cutoff frequency (Hz)",
+            },
+        },
+    },
+    "pitch_shift": {
+        "cls": PitchShift,
+        "label": "Pitch Shift",
+        "description": "Shift pitch up or down by semitones.",
+        "params": {
+            "semitones": {"default": 0.0, "min": -12.0, "max": 12.0, "step": 0.5, "description": "Semitones to shift"},
+        },
+    },
+}
+
+
+BUILTIN_PRESETS: Dict[str, Dict[str, Any]] = {
+    "robotic": {
+        "name": "Robotic",
+        "sort_order": 0,
+        "description": "Metallic robotic voice (flanger with slow LFO and high feedback)",
+        "effects_chain": [
+            {
+                "type": "chorus",
+                "enabled": True,
+                "params": {
+                    "rate_hz": 0.2,
+                    "depth": 1.0,
+                    "feedback": 0.35,
+                    "centre_delay_ms": 7.0,
+                    "mix": 0.5,
+                },
+            },
+        ],
+    },
+    "radio": {
+        "name": "Radio",
+        "sort_order": 1,
+        "description": "Thin AM-radio voice with band-pass filtering and light compression",
+        "effects_chain": [
+            {
+                "type": "highpass",
+                "enabled": True,
+                "params": {"cutoff_frequency_hz": 300.0},
+            },
+            {
+                "type": "lowpass",
+                "enabled": True,
+                "params": {"cutoff_frequency_hz": 3500.0},
+            },
+            {
+                "type": "compressor",
+                "enabled": True,
+                "params": {
+                    "threshold_db": -15.0,
+                    "ratio": 6.0,
+                    "attack_ms": 5.0,
+                    "release_ms": 50.0,
+                },
+            },
+            {
+                "type": "gain",
+                "enabled": True,
+                "params": {"gain_db": 6.0},
+            },
+        ],
+    },
+    "echo_chamber": {
+        "name": "Echo Chamber",
+        "sort_order": 2,
+        "description": "Spacious reverb with trailing echo",
+        "effects_chain": [
+            {
+                "type": "reverb",
+                "enabled": True,
+                "params": {
+                    "room_size": 0.85,
+                    "damping": 0.3,
+                    "wet_level": 0.45,
+                    "dry_level": 0.55,
+                    "width": 1.0,
+                },
+            },
+            {
+                "type": "delay",
+                "enabled": True,
+                "params": {
+                    "delay_seconds": 0.25,
+                    "feedback": 0.3,
+                    "mix": 0.2,
+                },
+            },
+        ],
+    },
+    "deep_voice": {
+        "name": "Deep Voice",
+        "sort_order": 99,
+        "description": "Lower pitch with added warmth",
+        "effects_chain": [
+            {
+                "type": "pitch_shift",
+                "enabled": True,
+                "params": {"semitones": -3.0},
+            },
+            {
+                "type": "lowpass",
+                "enabled": True,
+                "params": {"cutoff_frequency_hz": 6000.0},
+            },
+            {
+                "type": "compressor",
+                "enabled": True,
+                "params": {
+                    "threshold_db": -18.0,
+                    "ratio": 3.0,
+                    "attack_ms": 10.0,
+                    "release_ms": 150.0,
+                },
+            },
+        ],
+    },
+}
+
+
+def get_available_effects() -> List[Dict[str, Any]]:
+    """Return the list of available effect types with their parameter definitions.
+
+    Used by the frontend to build the effects chain editor UI.
+    """
+    result = []
+    for effect_type, info in EFFECT_REGISTRY.items():
+        result.append(
+            {
+                "type": effect_type,
+                "label": info["label"],
+                "description": info["description"],
+                "params": {name: {k: v for k, v in pdef.items()} for name, pdef in info["params"].items()},
+            }
+        )
+    return result
+
+
+def get_builtin_presets() -> Dict[str, Dict[str, Any]]:
+    """Return all built-in effect presets."""
+    return BUILTIN_PRESETS
+
+
+def validate_effects_chain(effects_chain: List[Dict[str, Any]]) -> Optional[str]:
+    """Validate an effects chain configuration.
+
+    Returns None if valid, or an error message string.
+    """
+    if not isinstance(effects_chain, list):
+        return "effects_chain must be a list"
+
+    for i, effect in enumerate(effects_chain):
+        if not isinstance(effect, dict):
+            return f"Effect at index {i} must be a dict"
+
+        effect_type = effect.get("type")
+        if effect_type not in EFFECT_REGISTRY:
+            return f"Unknown effect type '{effect_type}' at index {i}. Available: {list(EFFECT_REGISTRY.keys())}"
+
+        params = effect.get("params", {})
+        if not isinstance(params, dict):
+            return f"Effect '{effect_type}' at index {i}: params must be a dict"
+
+        registry = EFFECT_REGISTRY[effect_type]
+        for param_name, value in params.items():
+            if param_name not in registry["params"]:
+                return f"Effect '{effect_type}' at index {i}: unknown param '{param_name}'"
+
+            pdef = registry["params"][param_name]
+            if not isinstance(value, (int, float)):
+                return f"Effect '{effect_type}' at index {i}: param '{param_name}' must be a number"
+            if value < pdef["min"] or value > pdef["max"]:
+                return (
+                    f"Effect '{effect_type}' at index {i}: param '{param_name}' "
+                    f"must be between {pdef['min']} and {pdef['max']} (got {value})"
+                )
+
+    return None
+
+
+def build_pedalboard(effects_chain: List[Dict[str, Any]]) -> Pedalboard:
+    """Build a Pedalboard instance from an effects chain config.
+
+    Skips effects where ``enabled`` is ``False``.
+    """
+    plugins = []
+    for effect in effects_chain:
+        if not effect.get("enabled", True):
+            continue
+
+        effect_type = effect["type"]
+        registry = EFFECT_REGISTRY[effect_type]
+        cls = registry["cls"]
+
+        # Merge defaults with provided params
+        params = {}
+        for pname, pdef in registry["params"].items():
+            params[pname] = effect.get("params", {}).get(pname, pdef["default"])
+
+        plugins.append(cls(**params))
+
+    return Pedalboard(plugins)
+
+
+def apply_effects(
+    audio: np.ndarray,
+    sample_rate: int,
+    effects_chain: List[Dict[str, Any]],
+) -> np.ndarray:
+    """Apply an effects chain to audio data.
+
+    Args:
+        audio: Input audio array (1-D mono float32).
+        sample_rate: Sample rate in Hz.
+        effects_chain: List of effect configuration dicts.
+
+    Returns:
+        Processed audio array.
+    """
+    if not effects_chain:
+        return audio
+
+    board = build_pedalboard(effects_chain)
+
+    # pedalboard expects shape (channels, samples)
+    if audio.ndim == 1:
+        audio_2d = audio[np.newaxis, :]
+    else:
+        audio_2d = audio
+
+    processed = board(audio_2d.astype(np.float32), sample_rate)
+
+    # Return same dimensionality as input
+    if audio.ndim == 1:
+        return processed[0]
+    return processed

backend/utils/hf_offline_patch.py

@@ -0,0 +1,270 @@
+"""Monkey-patch huggingface_hub to force offline mode with cached models.
+
+Prevents mlx_audio / transformers from making network requests when models
+are already downloaded. Must be imported BEFORE mlx_audio.
+"""
+
+import logging
+import os
+import threading
+from contextlib import contextmanager
+from pathlib import Path
+from typing import Optional, Union
+
+logger = logging.getLogger(__name__)
+
+
+# huggingface_hub reads ``HF_HUB_OFFLINE`` once at import time into
+# ``huggingface_hub.constants.HF_HUB_OFFLINE``; transformers mirrors that into
+# ``transformers.utils.hub._is_offline_mode`` at *its* import time. Toggling
+# ``os.environ`` after either module is imported does not flip those cached
+# bools, and the hot paths (``_http._default_backend_factory``,
+# ``transformers.utils.hub.is_offline_mode``) read the bools — not the env.
+# We mutate the cached constants directly, guarded by a refcount so
+# concurrent inference threads share a single offline window safely.
+
+_offline_lock = threading.RLock()
+_offline_refcount = 0
+_saved_env: Optional[str] = None
+_saved_hf_const: Optional[bool] = None
+_saved_transformers_const: Optional[bool] = None
+
+
+@contextmanager
+def force_offline_if_cached(is_cached: bool, model_label: str = ""):
+    """Force offline mode for the duration of a cached-model operation.
+
+    Flips ``HF_HUB_OFFLINE`` in the process env **and** in the cached bools
+    inside ``huggingface_hub.constants`` / ``transformers.utils.hub`` so HTTP
+    adapters and offline-mode checks actually see the change. Uses a refcount
+    so multiple concurrent inference threads share a single offline window
+    and the last one to exit restores state.
+
+    If *is_cached* is ``False`` the block runs normally (network allowed).
+
+    Args:
+        is_cached: Whether the model weights are already on disk.
+        model_label: Human-readable name used in log messages.
+    """
+    if not is_cached:
+        yield
+        return
+
+    global _offline_refcount, _saved_env, _saved_hf_const, _saved_transformers_const
+
+    with _offline_lock:
+        if _offline_refcount == 0:
+            # Snapshot prior state, apply new state, roll back on *any*
+            # failure. Catching only ImportError here would let a partially
+            # broken install (RuntimeError, AttributeError from a half-init
+            # module, etc.) leave the cached HF constants mutated without
+            # bumping the refcount — a persistent offline leak that outlives
+            # the process and is miserable to debug.
+            prev_env = os.environ.get("HF_HUB_OFFLINE")
+            prev_hf: Optional[bool] = None
+            prev_tf: Optional[bool] = None
+            try:
+                try:
+                    import huggingface_hub.constants as hf_const
+
+                    prev_hf = hf_const.HF_HUB_OFFLINE
+                    hf_const.HF_HUB_OFFLINE = True
+                except ImportError:
+                    prev_hf = None
+
+                try:
+                    import transformers.utils.hub as tf_hub
+
+                    prev_tf = tf_hub._is_offline_mode
+                    tf_hub._is_offline_mode = True
+                except ImportError:
+                    prev_tf = None
+
+                os.environ["HF_HUB_OFFLINE"] = "1"
+            except BaseException:
+                # Roll back whatever we already changed, then re-raise so
+                # the caller sees the real failure.
+                if prev_hf is not None:
+                    try:
+                        import huggingface_hub.constants as hf_const
+
+                        hf_const.HF_HUB_OFFLINE = prev_hf
+                    except ImportError:
+                        pass
+                if prev_tf is not None:
+                    try:
+                        import transformers.utils.hub as tf_hub
+
+                        tf_hub._is_offline_mode = prev_tf
+                    except ImportError:
+                        pass
+                if prev_env is not None:
+                    os.environ["HF_HUB_OFFLINE"] = prev_env
+                else:
+                    os.environ.pop("HF_HUB_OFFLINE", None)
+                raise
+
+            _saved_env = prev_env
+            _saved_hf_const = prev_hf
+            _saved_transformers_const = prev_tf
+            logger.info(
+                "[offline-guard] %s is cached — forcing offline mode",
+                model_label or "model",
+            )
+        _offline_refcount += 1
+
+    try:
+        yield
+    finally:
+        with _offline_lock:
+            _offline_refcount -= 1
+            if _offline_refcount == 0:
+                if _saved_env is not None:
+                    os.environ["HF_HUB_OFFLINE"] = _saved_env
+                else:
+                    os.environ.pop("HF_HUB_OFFLINE", None)
+                if _saved_hf_const is not None:
+                    try:
+                        import huggingface_hub.constants as hf_const
+
+                        hf_const.HF_HUB_OFFLINE = _saved_hf_const
+                    except ImportError:
+                        pass
+                if _saved_transformers_const is not None:
+                    try:
+                        import transformers.utils.hub as tf_hub
+
+                        tf_hub._is_offline_mode = _saved_transformers_const
+                    except ImportError:
+                        pass
+                _saved_env = None
+                _saved_hf_const = None
+                _saved_transformers_const = None
+
+
+_mistral_regex_patched = False
+
+
+def patch_transformers_mistral_regex():
+    """Make transformers' tokenizer load robust to HuggingFace metadata failures.
+
+    transformers 4.57.x added ``PreTrainedTokenizerBase._patch_mistral_regex``
+    which unconditionally calls ``huggingface_hub.model_info(repo_id)`` during
+    every non-local tokenizer load to check whether the model is a Mistral
+    variant. That call raises on ``HF_HUB_OFFLINE=1`` and on plain network
+    failures, killing unrelated loads (Qwen TTS, TADA, etc.).
+
+    Voicebox never loads Mistral models, so the rewrite the function would
+    apply is a no-op for us anyway. Wrap the method so any exception from the
+    metadata lookup returns the tokenizer unchanged — matching the success-path
+    behavior for non-Mistral repos (transformers 4.57.3,
+    ``tokenization_utils_base.py:2503``).
+    """
+    global _mistral_regex_patched
+    if _mistral_regex_patched:
+        return
+
+    try:
+        from transformers.tokenization_utils_base import PreTrainedTokenizerBase
+    except ImportError:
+        logger.debug("transformers not available, skipping mistral-regex patch")
+        return
+
+    original = getattr(PreTrainedTokenizerBase, "_patch_mistral_regex", None)
+    if original is None:
+        logger.debug(
+            "transformers has no _patch_mistral_regex attribute, skipping patch",
+        )
+        return
+
+    def safe_patch_mistral_regex(cls, tokenizer, pretrained_model_name_or_path, *args, **kwargs):
+        try:
+            return original(tokenizer, pretrained_model_name_or_path, *args, **kwargs)
+        except Exception as exc:
+            logger.debug(
+                "[mistral-regex-patch] suppressed %s for %r, returning tokenizer as-is",
+                type(exc).__name__,
+                pretrained_model_name_or_path,
+            )
+            return tokenizer
+
+    PreTrainedTokenizerBase._patch_mistral_regex = classmethod(safe_patch_mistral_regex)
+    _mistral_regex_patched = True
+    logger.debug("installed _patch_mistral_regex wrapper")
+
+
+def patch_huggingface_hub_offline():
+    """Monkey-patch huggingface_hub to force offline mode."""
+    try:
+        import huggingface_hub  # noqa: F401 -- need the package loaded
+        from huggingface_hub import constants as hf_constants
+        from huggingface_hub.file_download import _try_to_load_from_cache
+
+        original_try_load = _try_to_load_from_cache
+
+        def _patched_try_to_load_from_cache(
+            repo_id: str,
+            filename: str,
+            cache_dir: Union[str, Path, None] = None,
+            revision: Optional[str] = None,
+            repo_type: Optional[str] = None,
+        ):
+            result = original_try_load(
+                repo_id=repo_id,
+                filename=filename,
+                cache_dir=cache_dir,
+                revision=revision,
+                repo_type=repo_type,
+            )
+
+            if result is None:
+                cache_path = Path(hf_constants.HF_HUB_CACHE) / f"models--{repo_id.replace('/', '--')}"
+                logger.debug("file not cached: %s/%s (expected at %s)", repo_id, filename, cache_path)
+            else:
+                logger.debug("cache hit: %s/%s", repo_id, filename)
+
+            return result
+
+        import huggingface_hub.file_download as fd
+
+        fd._try_to_load_from_cache = _patched_try_to_load_from_cache
+        logger.debug("huggingface_hub patched for offline mode")
+
+    except ImportError:
+        logger.debug("huggingface_hub not available, skipping offline patch")
+    except Exception:
+        logger.exception("failed to patch huggingface_hub for offline mode")
+
+
+def ensure_original_qwen_config_cached():
+    """Symlink the original Qwen repo cache to the MLX community version.
+
+    mlx_audio may try to fetch config from the original Qwen repo. If only
+    the MLX community variant is cached, create a symlink so the cache lookup
+    succeeds without a network request.
+    """
+    try:
+        from huggingface_hub import constants as hf_constants
+    except ImportError:
+        return
+
+    original_repo = "Qwen/Qwen3-TTS-12Hz-1.7B-Base"
+    mlx_repo = "mlx-community/Qwen3-TTS-12Hz-1.7B-Base-bf16"
+
+    cache_dir = Path(hf_constants.HF_HUB_CACHE)
+    original_path = cache_dir / f"models--{original_repo.replace('/', '--')}"
+    mlx_path = cache_dir / f"models--{mlx_repo.replace('/', '--')}"
+
+    if not original_path.exists() and mlx_path.exists():
+        try:
+            original_path.parent.mkdir(parents=True, exist_ok=True)
+            original_path.symlink_to(mlx_path, target_is_directory=True)
+            logger.info("created cache symlink: %s -> %s", original_repo, mlx_repo)
+        except Exception:
+            logger.warning("could not create cache symlink for %s", original_repo, exc_info=True)
+
+
+if os.environ.get("VOICEBOX_OFFLINE_PATCH", "1") != "0":
+    patch_huggingface_hub_offline()
+    patch_transformers_mistral_regex()
+    ensure_original_qwen_config_cached()

backend/utils/hf_progress.py

@@ -0,0 +1,383 @@
+"""
+HuggingFace Hub download progress tracking.
+"""
+
+from typing import Optional, Callable
+from contextlib import contextmanager
+import logging
+import threading
+import sys
+
+logger = logging.getLogger(__name__)
+
+
+class HFProgressTracker:
+    """Tracks HuggingFace Hub download progress by intercepting tqdm."""
+
+    def __init__(self, progress_callback: Optional[Callable] = None, filter_non_downloads: bool = False):
+        self.progress_callback = progress_callback
+        self.filter_non_downloads = filter_non_downloads  # Only filter if True
+        self._original_tqdm_class = None
+        self._lock = threading.Lock()
+        self._total_downloaded = 0
+        self._total_size = 0
+        self._file_sizes = {}  # Track sizes of individual files
+        self._file_downloaded = {}  # Track downloaded bytes per file
+        self._current_filename = ""
+        self._active_tqdms = {}  # Track active tqdm instances
+        self._hf_tqdm_original_update = None  # For monkey-patching hf's tqdm
+
+    def _create_tracked_tqdm_class(self):
+        """Create a tqdm subclass that tracks progress."""
+        tracker = self
+        original_tqdm = self._original_tqdm_class
+
+        class TrackedTqdm(original_tqdm):
+            """A tqdm subclass that reports progress to our tracker."""
+
+            def __init__(self, *args, **kwargs):
+                # Extract filename from desc before passing to parent
+                desc = kwargs.get("desc", "")
+                if not desc and args:
+                    first_arg = args[0]
+                    if isinstance(first_arg, str):
+                        desc = first_arg
+
+                filename = ""
+                if desc:
+                    # Try to extract filename from description
+                    # HuggingFace Hub uses format like "model.safetensors: 0%|..."
+                    if ":" in desc:
+                        filename = desc.split(":")[0].strip()
+                    else:
+                        filename = desc.strip()
+
+                # Filter out non-standard kwargs that huggingface_hub might pass
+                # These are custom kwargs that tqdm doesn't understand
+                filtered_kwargs = {}
+                # Known tqdm kwargs - pass these through
+                tqdm_kwargs = {
+                    "iterable",
+                    "desc",
+                    "total",
+                    "leave",
+                    "file",
+                    "ncols",
+                    "mininterval",
+                    "maxinterval",
+                    "miniters",
+                    "ascii",
+                    "disable",
+                    "unit",
+                    "unit_scale",
+                    "dynamic_ncols",
+                    "smoothing",
+                    "bar_format",
+                    "initial",
+                    "position",
+                    "postfix",
+                    "unit_divisor",
+                    "write_bytes",
+                    "lock_args",
+                    "nrows",
+                    "colour",
+                    "color",
+                    "delay",
+                    "gui",
+                    "disable_default",
+                    "pos",
+                }
+                for key, value in kwargs.items():
+                    if key in tqdm_kwargs:
+                        filtered_kwargs[key] = value
+
+                # Force-enable the progress bar — we're tracking progress ourselves,
+                # we don't need tqdm to render to a terminal, but we DO need
+                # self.n to be updated when update() is called.
+                filtered_kwargs["disable"] = False
+
+                # Try to initialize with filtered kwargs, fall back to all kwargs if that fails
+                try:
+                    super().__init__(*args, **filtered_kwargs)
+                except TypeError:
+                    # If filtering failed, try with all kwargs (maybe tqdm version accepts them)
+                    kwargs["disable"] = False
+                    super().__init__(*args, **kwargs)
+
+                self._tracker_filename = filename or "unknown"
+
+                with tracker._lock:
+                    if filename:
+                        tracker._current_filename = filename
+                    tracker._active_tqdms[id(self)] = {
+                        "filename": self._tracker_filename,
+                    }
+
+            def update(self, n=1):
+                result = super().update(n)
+
+                # Report progress
+                with tracker._lock:
+                    if id(self) in tracker._active_tqdms:
+                        filename = tracker._active_tqdms[id(self)]["filename"]
+                        current = getattr(self, "n", 0)
+                        total = getattr(self, "total", 0)
+
+                        if total and total > 0:
+                            # Always filter out non-byte progress bars (e.g., "Fetching 12 files")
+                            # These cause crazy percentages because they're counting files, not bytes
+                            if self._is_non_byte_progress(filename):
+                                return result
+
+                            # When model is cached, also filter out generation-related progress
+                            if tracker.filter_non_downloads:
+                                if not self._is_download_progress(filename):
+                                    return result
+
+                            # Update per-file tracking
+                            tracker._file_sizes[filename] = total
+                            tracker._file_downloaded[filename] = current
+
+                            # Calculate totals across all files
+                            tracker._total_size = sum(tracker._file_sizes.values())
+                            tracker._total_downloaded = sum(tracker._file_downloaded.values())
+
+                            # Only report progress once we have a meaningful total (at least 1MB)
+                            # This avoids the "100% at 0MB" issue when small config
+                            # files are counted before the real model files
+                            MIN_TOTAL_BYTES = 1_000_000  # 1MB
+                            if tracker._total_size < MIN_TOTAL_BYTES:
+                                return result
+
+                            # Call progress callback
+                            if tracker.progress_callback:
+                                tracker.progress_callback(tracker._total_downloaded, tracker._total_size, filename)
+
+                return result
+
+            def _is_non_byte_progress(self, filename: str) -> bool:
+                """Check if this progress bar should be SKIPPED (returns True to skip).
+
+                We want to track byte-based progress bars. This method identifies
+                progress bars that count files/items instead of bytes, which would
+                cause crazy percentages if mixed with our byte counting.
+
+                Returns:
+                    True = SKIP this bar (it's not byte-based)
+                    False = TRACK this bar (it counts bytes)
+                """
+                if not filename:
+                    return False
+
+                filename_lower = filename.lower()
+
+                # Skip "Fetching X files" - it counts files (total=12), not bytes
+                # Don't skip "Downloading (incomplete total...)" - that IS byte-based
+                skip_patterns = [
+                    "fetching",  # "Fetching 12 files" has total=12 files, not bytes
+                ]
+                return any(pattern in filename_lower for pattern in skip_patterns)
+
+            def _is_download_progress(self, filename: str) -> bool:
+                """Check if this is a real file download progress bar vs internal processing."""
+                if not filename or filename == "unknown":
+                    return False
+
+                # Real downloads have file extensions
+                download_extensions = [
+                    ".safetensors",
+                    ".bin",
+                    ".pt",
+                    ".pth",  # Model weights
+                    ".json",
+                    ".txt",
+                    ".py",  # Config files
+                    ".msgpack",
+                    ".h5",  # Other formats
+                ]
+
+                filename_lower = filename.lower()
+                has_extension = any(filename_lower.endswith(ext) for ext in download_extensions)
+
+                # Skip generation-related progress indicators
+                skip_patterns = ["segment", "processing", "generating", "loading"]
+                has_skip_pattern = any(pattern in filename_lower for pattern in skip_patterns)
+
+                return has_extension and not has_skip_pattern
+
+            def close(self):
+                with tracker._lock:
+                    if id(self) in tracker._active_tqdms:
+                        del tracker._active_tqdms[id(self)]
+                return super().close()
+
+        return TrackedTqdm
+
+    @contextmanager
+    def patch_download(self):
+        """Context manager to patch tqdm for progress tracking."""
+        try:
+            import tqdm as tqdm_module
+
+            # Store original tqdm class
+            self._original_tqdm_class = tqdm_module.tqdm
+
+            # Reset totals
+            with self._lock:
+                self._total_downloaded = 0
+                self._total_size = 0
+                self._file_sizes = {}
+                self._file_downloaded = {}
+                self._current_filename = ""
+                self._active_tqdms = {}
+
+            # Create our tracked tqdm class
+            tracked_tqdm = self._create_tracked_tqdm_class()
+
+            # Patch tqdm.tqdm
+            tqdm_module.tqdm = tracked_tqdm
+
+            # Also patch tqdm.auto.tqdm if it exists (used by huggingface_hub)
+            self._original_tqdm_auto = None
+            if hasattr(tqdm_module, "auto") and hasattr(tqdm_module.auto, "tqdm"):
+                self._original_tqdm_auto = tqdm_module.auto.tqdm
+                tqdm_module.auto.tqdm = tracked_tqdm
+
+            # Patch in sys.modules to catch already-imported references
+            # huggingface_hub uses: from tqdm.auto import tqdm as base_tqdm
+            # So we need to patch both 'tqdm' and 'base_tqdm' attributes
+            self._patched_modules = {}
+            tqdm_attr_names = ["tqdm", "base_tqdm", "old_tqdm"]  # Various names used
+
+            patched_count = 0
+            for module_name in list(sys.modules.keys()):
+                if "huggingface" in module_name or module_name.startswith("tqdm"):
+                    try:
+                        module = sys.modules[module_name]
+                        for attr_name in tqdm_attr_names:
+                            if hasattr(module, attr_name):
+                                attr = getattr(module, attr_name)
+                                # Only patch if it's a tqdm class (not already patched)
+                                is_tqdm_class = (
+                                    attr is self._original_tqdm_class
+                                    or (self._original_tqdm_auto and attr is self._original_tqdm_auto)
+                                    or (
+                                        hasattr(attr, "__name__")
+                                        and attr.__name__ == "tqdm"
+                                        and hasattr(attr, "update")
+                                    )  # tqdm classes have update method
+                                )
+                                if is_tqdm_class:
+                                    key = f"{module_name}.{attr_name}"
+                                    self._patched_modules[key] = (module, attr_name, attr)
+                                    setattr(module, attr_name, tracked_tqdm)
+                                    patched_count += 1
+                    except (AttributeError, TypeError):
+                        pass
+
+            # ALSO monkey-patch the update method on huggingface_hub's tqdm class
+            # This is needed because the class was already defined at import time
+            self._hf_tqdm_original_update = None
+            try:
+                from huggingface_hub.utils import tqdm as hf_tqdm_module
+
+                if hasattr(hf_tqdm_module, "tqdm"):
+                    hf_tqdm_class = hf_tqdm_module.tqdm
+                    self._hf_tqdm_original_update = hf_tqdm_class.update
+
+                    # Create a wrapper that calls our tracking
+                    tracker = self  # Reference to HFProgressTracker instance
+
+                    def patched_update(tqdm_self, n=1):
+                        result = tracker._hf_tqdm_original_update(tqdm_self, n)
+
+                        # Track this progress
+                        with tracker._lock:
+                            desc = getattr(tqdm_self, "desc", "") or ""
+                            current = getattr(tqdm_self, "n", 0)
+                            total = getattr(tqdm_self, "total", 0) or 0
+
+                            # Skip non-byte progress bars
+                            if "fetching" in desc.lower():
+                                return result
+
+                            # Skip until we have a meaningful total (at least 1MB)
+                            # This avoids the "100% at 0MB" issue when small config
+                            # files are counted before the real model files
+                            MIN_TOTAL_BYTES = 1_000_000  # 1MB
+                            if total >= MIN_TOTAL_BYTES:
+                                tracker._total_downloaded = current
+                                tracker._total_size = total
+
+                                if tracker.progress_callback:
+                                    tracker.progress_callback(current, total, desc)
+
+                        return result
+
+                    hf_tqdm_class.update = patched_update
+                    patched_count += 1
+                    logger.debug("Monkey-patched huggingface_hub.utils.tqdm.tqdm.update")
+            except (ImportError, AttributeError) as e:
+                logger.warning("Could not monkey-patch hf_tqdm: %s", e)
+
+            logger.debug("Patched %d tqdm references", patched_count)
+
+            yield
+
+        except ImportError:
+            # If tqdm not available, just yield without patching
+            yield
+        finally:
+            # Restore original tqdm
+            if self._original_tqdm_class:
+                try:
+                    import tqdm as tqdm_module
+
+                    tqdm_module.tqdm = self._original_tqdm_class
+
+                    if self._original_tqdm_auto:
+                        tqdm_module.auto.tqdm = self._original_tqdm_auto
+
+                    # Restore patched modules
+                    for key, (module, attr_name, original) in self._patched_modules.items():
+                        try:
+                            if module and original:
+                                setattr(module, attr_name, original)
+                        except (AttributeError, TypeError):
+                            pass
+                    self._patched_modules = {}
+
+                    # Restore hf_tqdm's original update method
+                    if self._hf_tqdm_original_update:
+                        try:
+                            from huggingface_hub.utils import tqdm as hf_tqdm_module
+
+                            if hasattr(hf_tqdm_module, "tqdm"):
+                                hf_tqdm_module.tqdm.update = self._hf_tqdm_original_update
+                        except (ImportError, AttributeError):
+                            pass
+                        self._hf_tqdm_original_update = None
+
+                except (ImportError, AttributeError):
+                    pass
+
+
+def create_hf_progress_callback(model_name: str, progress_manager):
+    """Create a progress callback for HuggingFace downloads."""
+
+    def callback(downloaded: int, total: int, filename: str = ""):
+        """Progress callback.
+
+        Note: We send updates even when total=0 (unknown) to provide feedback
+        during the "incomplete total" phase of huggingface_hub downloads.
+        The frontend handles total=0 gracefully.
+        """
+        progress_manager.update_progress(
+            model_name=model_name,
+            current=downloaded,
+            total=total,
+            filename=filename or "",
+            status="downloading",
+        )
+
+    return callback

backend/utils/images.py

@@ -0,0 +1,114 @@
+"""Image processing utilities for avatar uploads."""
+
+from pathlib import Path
+from typing import Optional, Tuple
+from PIL import Image
+
+# JPEG can be reported as 'JPEG' or 'MPO' (for multi-picture format from some cameras)
+ALLOWED_FORMATS = {'PNG', 'JPEG', 'WEBP', 'MPO', 'JPG'}
+MAX_SIZE = 512
+MAX_FILE_SIZE = 5 * 1024 * 1024  # 5MB
+
+
+def validate_image(file_path: str) -> Tuple[bool, Optional[str]]:
+    """
+    Validate image format and file size.
+    
+    Args:
+        file_path: Path to image file
+        
+    Returns:
+        Tuple of (is_valid, error_message)
+    """
+    path = Path(file_path)
+    
+    # Check file size
+    if path.stat().st_size > MAX_FILE_SIZE:
+        return False, f"File size exceeds maximum of {MAX_FILE_SIZE // (1024 * 1024)}MB"
+    
+    try:
+        with Image.open(file_path) as img:
+            # Verify the image can be loaded
+            img.load()
+            
+            # Check format (normalize JPEG variants)
+            img_format = img.format
+            if img_format in ('MPO', 'JPG'):
+                img_format = 'JPEG'
+            
+            if img_format not in {'PNG', 'JPEG', 'WEBP'}:
+                return False, f"Invalid format '{img_format}'. Allowed formats: PNG, JPEG, WEBP"
+            
+            return True, None
+    except Exception as e:
+        return False, f"Invalid image file: {str(e)}"
+
+
+def process_avatar(input_path: str, output_path: str, max_size: int = MAX_SIZE) -> None:
+    """
+    Process avatar image: resize and optimize.
+    
+    Resizes image to fit within max_size x max_size while maintaining aspect ratio.
+    
+    Args:
+        input_path: Path to input image
+        output_path: Path to save processed image
+        max_size: Maximum width or height in pixels
+    """
+    with Image.open(input_path) as img:
+        # Handle EXIF orientation for JPEG images
+        try:
+            from PIL import ExifTags
+            for orientation in ExifTags.TAGS.keys():
+                if ExifTags.TAGS[orientation] == 'Orientation':
+                    break
+            exif = img._getexif()
+            if exif is not None:
+                orientation_value = exif.get(orientation)
+                if orientation_value == 3:
+                    img = img.rotate(180, expand=True)
+                elif orientation_value == 6:
+                    img = img.rotate(270, expand=True)
+                elif orientation_value == 8:
+                    img = img.rotate(90, expand=True)
+        except (AttributeError, KeyError, IndexError, TypeError):
+            # No EXIF data or orientation tag
+            pass
+        
+        # Convert to RGB if necessary (handles RGBA, P, CMYK, etc.)
+        if img.mode not in ('RGB', 'L'):
+            if img.mode == 'RGBA':
+                # Create white background for RGBA images
+                background = Image.new('RGB', img.size, (255, 255, 255))
+                background.paste(img, mask=img.split()[3])  # Use alpha channel as mask
+                img = background
+            elif img.mode == 'CMYK':
+                # Convert CMYK to RGB
+                img = img.convert('RGB')
+            elif img.mode == 'P':
+                # Convert palette mode to RGB
+                img = img.convert('RGB')
+            else:
+                img = img.convert('RGB')
+        
+        # Calculate new size maintaining aspect ratio
+        img.thumbnail((max_size, max_size), Image.Resampling.LANCZOS)
+        
+        # Determine output format from extension
+        output_ext = Path(output_path).suffix.lower()
+        
+        format_map = {
+            '.png': 'PNG',
+            '.jpeg': 'JPEG',
+            '.jpg': 'JPEG',
+            '.webp': 'WEBP'
+        }
+        
+        output_format = format_map.get(output_ext, 'PNG')
+        
+        # Save with optimization
+        save_kwargs = {'optimize': True}
+        if output_format == 'JPEG':
+            save_kwargs['quality'] = 90
+        
+        img.save(output_path, format=output_format, **save_kwargs)

backend/utils/platform_detect.py

@@ -0,0 +1,35 @@
+"""
+Platform detection for backend selection.
+"""
+
+import platform
+from typing import Literal
+
+
+def is_apple_silicon() -> bool:
+    """
+    Check if running on Apple Silicon (arm64 macOS).
+    
+    Returns:
+        True if on Apple Silicon, False otherwise
+    """
+    return platform.system() == "Darwin" and platform.machine() == "arm64"
+
+
+def get_backend_type() -> Literal["mlx", "pytorch"]:
+    """
+    Detect the best backend for the current platform.
+
+    Returns:
+        "mlx" on Apple Silicon (if MLX is available and functional), "pytorch" otherwise
+    """
+    if is_apple_silicon():
+        try:
+            import mlx.core  # noqa: F401 — triggers native lib loading
+            return "mlx"
+        except (ImportError, OSError, RuntimeError):
+            # MLX not installed, or native libraries failed to load inside a
+            # PyInstaller bundle (OSError on missing .dylib / .metallib).
+            # Fall through to PyTorch.
+            return "pytorch"
+    return "pytorch"

backend/utils/progress.py

@@ -0,0 +1,315 @@
+"""
+Progress tracking for model downloads using Server-Sent Events.
+"""
+
+from typing import Optional, Callable, Dict, List
+from fastapi.responses import StreamingResponse
+import asyncio
+import json
+import threading
+from datetime import datetime
+
+
+class ProgressManager:
+    """Manages download progress for multiple models.
+    
+    Thread-safe: can be called from background threads (e.g., via asyncio.to_thread).
+    """
+    
+    # Throttle settings to prevent overwhelming SSE clients
+    THROTTLE_INTERVAL_SECONDS = 0.5  # Minimum time between updates
+    THROTTLE_PROGRESS_DELTA = 1.0    # Minimum progress change (%) to force update
+    
+    def __init__(self):
+        self._progress: Dict[str, Dict] = {}
+        self._listeners: Dict[str, list] = {}
+        self._lock = threading.Lock()  # Thread-safe lock for progress dict
+        self._main_loop: Optional[asyncio.AbstractEventLoop] = None
+        self._last_notify_time: Dict[str, float] = {}  # Last notification time per model
+        self._last_notify_progress: Dict[str, float] = {}  # Last notified progress per model
+    
+    def _set_main_loop(self, loop: asyncio.AbstractEventLoop):
+        """Set the main event loop for thread-safe operations."""
+        self._main_loop = loop
+    
+    def _notify_listeners_threadsafe(self, model_name: str, progress_data: Dict):
+        """Notify listeners in a thread-safe manner."""
+        import logging
+        logger = logging.getLogger(__name__)
+        
+        if model_name not in self._listeners:
+            return
+            
+        for queue in self._listeners[model_name]:
+            try:
+                # Check if we're in the main event loop thread
+                try:
+                    running_loop = asyncio.get_running_loop()
+                    # We're in an async context, can use put_nowait directly
+                    queue.put_nowait(progress_data.copy())
+                except RuntimeError:
+                    # Not in async context (running in background thread)
+                    # Use call_soon_threadsafe to safely put on queue
+                    if self._main_loop and self._main_loop.is_running():
+                        self._main_loop.call_soon_threadsafe(
+                            lambda q=queue, d=progress_data.copy(): q.put_nowait(d) if not q.full() else None
+                        )
+                    else:
+                        logger.debug(f"No main loop available for {model_name}, skipping notification")
+            except asyncio.QueueFull:
+                logger.warning(f"Queue full for {model_name}, dropping update")
+            except Exception as e:
+                logger.warning(f"Error notifying listener for {model_name}: {e}")
+
+    def update_progress(
+        self,
+        model_name: str,
+        current: int,
+        total: int,
+        filename: Optional[str] = None,
+        status: str = "downloading",
+    ):
+        """
+        Update progress for a model download.
+
+        Thread-safe: can be called from background threads.
+        
+        Progress updates are throttled to prevent overwhelming SSE clients.
+        Updates are sent at most every THROTTLE_INTERVAL_SECONDS, or when
+        progress changes by at least THROTTLE_PROGRESS_DELTA percent.
+
+        Args:
+            model_name: Name of the model (e.g., "qwen-tts-1.7B", "whisper-base")
+            current: Current bytes downloaded
+            total: Total bytes to download
+            filename: Current file being downloaded
+            status: Status string (downloading, extracting, complete, error)
+        """
+        import logging
+        import time
+        logger = logging.getLogger(__name__)
+
+        # Calculate progress percentage, clamped to 0-100 range
+        # This prevents crazy percentages from edge cases like:
+        # - current > total temporarily during aggregation
+        # - mixing file-count progress with byte-count progress
+        if total > 0:
+            progress_pct = min(100.0, max(0.0, (current / total * 100)))
+        else:
+            progress_pct = 0
+
+        progress_data = {
+            "model_name": model_name,
+            "current": current,
+            "total": total,
+            "progress": progress_pct,
+            "filename": filename,
+            "status": status,
+            "timestamp": datetime.now().isoformat(),
+        }
+
+        # Thread-safe update of progress dict (always update internal state)
+        with self._lock:
+            self._progress[model_name] = progress_data
+
+        # Check if we should notify listeners (throttling)
+        current_time = time.time()
+        last_time = self._last_notify_time.get(model_name, 0)
+        last_progress = self._last_notify_progress.get(model_name, -100)
+        
+        time_delta = current_time - last_time
+        progress_delta = abs(progress_pct - last_progress)
+        
+        # Always notify for complete/error status, or if throttle conditions are met
+        should_notify = (
+            status in ("complete", "error") or
+            time_delta >= self.THROTTLE_INTERVAL_SECONDS or
+            progress_delta >= self.THROTTLE_PROGRESS_DELTA
+        )
+        
+        if not should_notify:
+            return  # Skip this update (throttled)
+        
+        # Update throttle tracking
+        self._last_notify_time[model_name] = current_time
+        self._last_notify_progress[model_name] = progress_pct
+
+        # Notify all listeners (thread-safe)
+        listener_count = len(self._listeners.get(model_name, []))
+
+        if listener_count > 0:
+            logger.debug(f"Notifying {listener_count} listeners for {model_name}: {progress_pct:.1f}% ({filename})")
+            self._notify_listeners_threadsafe(model_name, progress_data)
+        else:
+            logger.debug(f"No listeners for {model_name}, progress update stored: {progress_pct:.1f}%")
+    
+    def get_progress(self, model_name: str) -> Optional[Dict]:
+        """Get current progress for a model. Thread-safe."""
+        with self._lock:
+            progress = self._progress.get(model_name)
+            return progress.copy() if progress else None
+    
+    def get_all_active(self) -> List[Dict]:
+        """Get all active downloads (status is 'downloading' or 'extracting'). Thread-safe."""
+        active = []
+        with self._lock:
+            for model_name, progress in self._progress.items():
+                status = progress.get("status", "")
+                if status in ("downloading", "extracting"):
+                    active.append(progress.copy())
+        return active
+    
+    def create_progress_callback(self, model_name: str, filename: Optional[str] = None):
+        """
+        Create a progress callback function for HuggingFace downloads.
+        
+        Args:
+            model_name: Name of the model
+            filename: Optional filename filter
+            
+        Returns:
+            Callback function
+        """
+        def callback(progress: Dict):
+            """HuggingFace Hub progress callback."""
+            if "total" in progress and "current" in progress:
+                current = progress.get("current", 0)
+                total = progress.get("total", 0)
+                file_name = progress.get("filename", filename)
+                
+                self.update_progress(
+                    model_name=model_name,
+                    current=current,
+                    total=total,
+                    filename=file_name,
+                    status="downloading",
+                )
+        
+        return callback
+    
+    async def subscribe(self, model_name: str):
+        """
+        Subscribe to progress updates for a model.
+
+        Yields progress updates as Server-Sent Events.
+        """
+        import logging
+        logger = logging.getLogger(__name__)
+        
+        # Store the main event loop for thread-safe operations
+        try:
+            self._main_loop = asyncio.get_running_loop()
+        except RuntimeError:
+            pass
+
+        queue = asyncio.Queue(maxsize=10)
+
+        # Add to listeners
+        if model_name not in self._listeners:
+            self._listeners[model_name] = []
+        self._listeners[model_name].append(queue)
+
+        logger.info(f"SSE client subscribed to {model_name}, total listeners: {len(self._listeners[model_name])}")
+
+        try:
+            # Send initial progress if available and still in progress (thread-safe read)
+            with self._lock:
+                initial_progress = self._progress.get(model_name)
+                if initial_progress:
+                    initial_progress = initial_progress.copy()
+            
+            if initial_progress:
+                status = initial_progress.get('status')
+                # Only send initial progress if download is actually in progress
+                # Don't send old 'complete' or 'error' status from previous downloads
+                if status in ('downloading', 'extracting'):
+                    logger.info(f"Sending initial progress for {model_name}: {status}")
+                    yield f"data: {json.dumps(initial_progress)}\n\n"
+                else:
+                    logger.info(f"Skipping initial progress for {model_name} (status: {status})")
+            else:
+                logger.info(f"No initial progress available for {model_name}")
+
+            # Stream updates
+            while True:
+                try:
+                    # Wait for update with timeout
+                    progress = await asyncio.wait_for(queue.get(), timeout=1.0)
+                    logger.debug(f"Sending progress update for {model_name}: {progress.get('status')} - {progress.get('progress', 0):.1f}%")
+                    yield f"data: {json.dumps(progress)}\n\n"
+
+                    # Stop if complete or error
+                    if progress.get("status") in ("complete", "error"):
+                        logger.info(f"Download {progress.get('status')} for {model_name}, closing SSE connection")
+                        break
+                except asyncio.TimeoutError:
+                    # Send heartbeat
+                    yield ": heartbeat\n\n"
+                    continue
+        except (BrokenPipeError, ConnectionResetError, asyncio.CancelledError):
+            logger.debug(f"SSE client disconnected from {model_name}")
+        finally:
+            # Remove from listeners
+            if model_name in self._listeners:
+                self._listeners[model_name].remove(queue)
+                if not self._listeners[model_name]:
+                    del self._listeners[model_name]
+                logger.info(f"SSE client unsubscribed from {model_name}, remaining listeners: {len(self._listeners.get(model_name, []))}")
+    
+    def mark_complete(self, model_name: str):
+        """Mark a model download as complete. Thread-safe."""
+        import logging
+        logger = logging.getLogger(__name__)
+
+        with self._lock:
+            if model_name in self._progress:
+                self._progress[model_name]["status"] = "complete"
+                self._progress[model_name]["progress"] = 100.0
+                progress_data = self._progress[model_name].copy()
+            else:
+                logger.warning(f"Cannot mark {model_name} as complete: not found in progress")
+                return
+        
+        logger.info(f"Marked {model_name} as complete")
+        # Notify listeners (thread-safe)
+        self._notify_listeners_threadsafe(model_name, progress_data)
+    
+    def mark_error(self, model_name: str, error: str):
+        """Mark a model download as failed. Thread-safe."""
+        import logging
+        logger = logging.getLogger(__name__)
+
+        with self._lock:
+            if model_name in self._progress:
+                self._progress[model_name]["status"] = "error"
+                self._progress[model_name]["error"] = error
+                progress_data = self._progress[model_name].copy()
+            else:
+                # Create new progress entry for error
+                progress_data = {
+                    "model_name": model_name,
+                    "current": 0,
+                    "total": 0,
+                    "progress": 0,
+                    "filename": None,
+                    "status": "error",
+                    "error": error,
+                    "timestamp": datetime.now().isoformat(),
+                }
+                self._progress[model_name] = progress_data
+        
+        logger.error(f"Marked {model_name} as error: {error}")
+        # Notify listeners (thread-safe)
+        self._notify_listeners_threadsafe(model_name, progress_data)
+
+
+# Global progress manager instance
+_progress_manager: Optional[ProgressManager] = None
+
+
+def get_progress_manager() -> ProgressManager:
+    """Get or create the global progress manager."""
+    global _progress_manager
+    if _progress_manager is None:
+        _progress_manager = ProgressManager()
+    return _progress_manager

backend/utils/tasks.py

@@ -0,0 +1,102 @@
+"""
+Task tracking for active downloads and generations.
+"""
+
+from typing import Optional, Dict, List
+from datetime import datetime
+from dataclasses import dataclass, field
+
+
+@dataclass
+class DownloadTask:
+    """Represents an active download task."""
+    model_name: str
+    status: str = "downloading"  # downloading, extracting, complete, error
+    started_at: datetime = field(default_factory=datetime.utcnow)
+    error: Optional[str] = None
+
+
+@dataclass
+class GenerationTask:
+    """Represents an active generation task."""
+    task_id: str
+    profile_id: str
+    text_preview: str  # First 50 chars of text
+    started_at: datetime = field(default_factory=datetime.utcnow)
+
+
+class TaskManager:
+    """Manages active downloads and generations."""
+    
+    def __init__(self):
+        self._active_downloads: Dict[str, DownloadTask] = {}
+        self._active_generations: Dict[str, GenerationTask] = {}
+    
+    def start_download(self, model_name: str) -> None:
+        """Mark a download as started."""
+        self._active_downloads[model_name] = DownloadTask(
+            model_name=model_name,
+            status="downloading",
+        )
+    
+    def complete_download(self, model_name: str) -> None:
+        """Mark a download as complete."""
+        if model_name in self._active_downloads:
+            del self._active_downloads[model_name]
+    
+    def error_download(self, model_name: str, error: str) -> None:
+        """Mark a download as failed."""
+        if model_name in self._active_downloads:
+            self._active_downloads[model_name].status = "error"
+            self._active_downloads[model_name].error = error
+    
+    def start_generation(self, task_id: str, profile_id: str, text: str) -> None:
+        """Mark a generation as started."""
+        text_preview = text[:50] + "..." if len(text) > 50 else text
+        self._active_generations[task_id] = GenerationTask(
+            task_id=task_id,
+            profile_id=profile_id,
+            text_preview=text_preview,
+        )
+    
+    def complete_generation(self, task_id: str) -> None:
+        """Mark a generation as complete."""
+        if task_id in self._active_generations:
+            del self._active_generations[task_id]
+    
+    def get_active_downloads(self) -> List[DownloadTask]:
+        """Get all active downloads."""
+        return list(self._active_downloads.values())
+    
+    def get_active_generations(self) -> List[GenerationTask]:
+        """Get all active generations."""
+        return list(self._active_generations.values())
+    
+    def cancel_download(self, model_name: str) -> bool:
+        """Cancel/dismiss a download task (removes it from active list)."""
+        return self._active_downloads.pop(model_name, None) is not None
+
+    def clear_all(self) -> None:
+        """Clear all download and generation tasks."""
+        self._active_downloads.clear()
+        self._active_generations.clear()
+
+    def is_download_active(self, model_name: str) -> bool:
+        """Check if a download is active."""
+        return model_name in self._active_downloads
+    
+    def is_generation_active(self, task_id: str) -> bool:
+        """Check if a generation is active."""
+        return task_id in self._active_generations
+
+
+# Global task manager instance
+_task_manager: Optional[TaskManager] = None
+
+
+def get_task_manager() -> TaskManager:
+    """Get or create the global task manager."""
+    global _task_manager
+    if _task_manager is None:
+        _task_manager = TaskManager()
+    return _task_manager